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Sample records for adaptive fuzzy controller

  1. Adaptive neuro-fuzzy controller of switched reluctance motor

    Directory of Open Access Journals (Sweden)

    Tahour Ahmed

    2007-01-01

    Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.

  2. Control of multi-machine using adaptive fuzzy

    Directory of Open Access Journals (Sweden)

    Bouchiba Bousmaha

    2011-01-01

    Full Text Available An indirect Adaptive fuzzy excitation control (IAFLC of power systems based on multi-input-multi-output linearization technique is developed in this paper. The power system considered in this paper consists of two generators and infinite bus connected through a network of transformers and transmission lines. The fuzzy controller is constructed from fuzzy feedback linearization controller whose parameters are adjusted indirectly from the estimates of plant parameters. The adaptation law adjusts the controller parameters on-line so that the plant output tracks the reference model output. Simulation results shown that the proposed controller IAFLC, compared with a controller based on tradition linearization technique can enhance the transient stability of the power system.

  3. Adaptive fuzzy controller based MPPT for photovoltaic systems

    International Nuclear Information System (INIS)

    Guenounou, Ouahib; Dahhou, Boutaib; Chabour, Ferhat

    2014-01-01

    Highlights: • We propose a fuzzy controller with adaptive output scaling factor as a maximum power point tracker of photovoltaic system. • The proposed controller integrates two different rule bases defined on error and change of error. • Our controller can track the maximum power point with better performances when compared to its conventional counterpart. - Abstract: This paper presents an intelligent approach to optimize the performances of photovoltaic systems. The system consists of a PV panel, a DC–DC boost converter, a maximum power point tracker controller and a resistive load. The key idea of the proposed approach is the use of a fuzzy controller with an adaptive gain as a maximum power point tracker. The proposed controller integrates two different rule bases. The first is used to adjust the duty cycle of the boost converter as in the case of a conventional fuzzy controller while the second rule base is designed for an online adjusting of the controller’s gain. The performances of the adaptive fuzzy controller are compared with those obtained using a conventional fuzzy controllers with different gains and in each case, the proposed controller outperforms its conventional counterpart

  4. Determination Of Adaptive Control Parameter Using Fuzzy Logic Controller

    Directory of Open Access Journals (Sweden)

    Omur Can Ozguney

    2017-08-01

    Full Text Available The robot industry has developed along with the increasing the use of robots in industry. This has led to increase the studies on robots. The most important part of these studies is that the robots must be work with minimum tracking trajectory error. But it is not easy for robots to track the desired trajectory because of the external disturbances and parametric uncertainty. Therefore adaptive and robust controllers are used to decrease tracking error. The aim of this study is to increase the tracking performance of the robot and minimize the trajectory tracking error. For this purpose adaptive control law for robot manipulator is identified and fuzzy logic controller is applied to find the accurate values for adaptive control parameter. Based on the Lyapunov theory stability of the uncertain system is guaranteed. In this study robot parameters are assumed to be unknown. This controller is applied to a robot model and the results of simulations are given. Controller with fuzzy logic and without fuzzy logic are compared with each other. Simulation results show that the fuzzy logic controller has improved the results.

  5. Adaptive learning fuzzy control of a mobile robot

    International Nuclear Information System (INIS)

    Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

    1989-11-01

    In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)

  6. Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence

    Directory of Open Access Journals (Sweden)

    Alejandro Carrasco Elizalde

    2008-01-01

    Full Text Available The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.

  7. Adaptive neuro-fuzzy control of ionic polymer metal composite actuators

    International Nuclear Information System (INIS)

    Thinh, Nguyen Truong; Yang, Young-Soo; Oh, Il-Kwon

    2009-01-01

    An adaptive neuro-fuzzy controller was newly designed to overcome the degradation of the actuation performance of ionic polymer metal composite actuators that show highly nonlinear responses such as a straightening-back problem under a step excitation. An adaptive control algorithm with the merits of fuzzy logic and neural networks was applied for controlling the tip displacement of the ionic polymer metal composite actuators. The reference and actual displacements and the change of the error with the electrical inputs were recorded to generate the training data. These data were used for training the adaptive neuro-fuzzy controller to find the membership functions in the fuzzy control algorithm. Software simulation and real-time experiments were conducted by using the Simulink and dSPACE environments. Present results show that the current adaptive neuro-fuzzy controller can be successfully applied to the reliable control of the ionic polymer metal composite actuator for which the performance degrades under long-time actuation

  8. Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure

    Directory of Open Access Journals (Sweden)

    Ashraf Ahmed Fahmy

    2014-03-01

    Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation.  Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.

  9. Indirect fuzzy adaptive control of a class of SISO nonlinear systems

    International Nuclear Information System (INIS)

    Laboid, S.; Boucherit, M.S.

    2006-01-01

    This paper presents an adaptive fuzzy control scheme for a class of continuous-time single-input single-output nonlinear systems with unknown dynamics and disturbance. Within this scheme, the fuzzy systems are employed to approximate the unknown system's dynamics. The proposed controller is composed of a well-defined adaptive fuzzy control term that uses the adaptive fuzzy approximation errors and disturbance. Based on a Lyapunov synthesis method, it is shown that the proposed adaptive control scheme guarantees the convergence of the tracking error to zero and the global boundedness of all signals in the closed-loop system. Moreover, the proposed controller allows initialization by zero of all adjusted parameters in the fuzzy approximators, and does not require the knowledge of the lower bound of the control gain and upper bounds of the approximation errors and disturbance. Simulation results performed on an inverted pendulum system are given to point out the good performance of the developed adaptive controller. (author)

  10. Adaptive fuzzy PID control for a quadrotor stabilisation

    Science.gov (United States)

    Cherrat, N.; Boubertakh, H.; Arioui, H.

    2018-02-01

    This paper deals with the design of an adaptive fuzzy PID control law for attitude and altitude stabilization of a quadrotor despite the system model uncertainties and disturbances. To this end, a PID control with adaptive gains is used in order to approximate a virtual ideal control previously designed to achieve the predefined objective. Specifically, the control gains are estimated and adjusted by mean of a fuzzy system whose parameters are adjusted online via a gradient descent-based adaptation law. The analysis of the closed-loop system is given by the Lyapunov approach. The simulation results are presented to illustrate the efficiency of the proposed approach.

  11. Fuzzy adaptive speed control of a permanent magnet synchronous motor

    Science.gov (United States)

    Choi, Han Ho; Jung, Jin-Woo; Kim, Rae-Young

    2012-05-01

    A fuzzy adaptive speed controller is proposed for a permanent magnet synchronous motor (PMSM). The proposed fuzzy adaptive speed regulator is insensitive to model parameter and load torque variations because it does not need any accurate knowledge about the motor parameter and load torque values. The stability of the proposed control system is also proven. The proposed adaptive speed regulator system is implemented by using a TMS320F28335 floating point DSP. Simulation and experimental results are presented to verify the effectiveness of the proposed fuzzy adaptive speed controller under uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.

  12. Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems

    International Nuclear Information System (INIS)

    Poursamad, Amir; Markazi, Amir H.D.

    2009-01-01

    This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.

  13. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    International Nuclear Information System (INIS)

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-01-01

    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller

  14. Adaptive fuzzy trajectory control for biaxial motion stage system

    Directory of Open Access Journals (Sweden)

    Wei-Lung Mao

    2016-04-01

    Full Text Available Motion control is an essential part of industrial machinery and manufacturing systems. In this article, the adaptive fuzzy controller is proposed for precision trajectory tracking control in biaxial X-Y motion stage system. The theoretical analyses of direct fuzzy control which is insensitive to parameter uncertainties and external load disturbances are derived to demonstrate the feasibility to track the reference trajectories. The Lyapunov stability theorem has been used to testify the asymptotic stability of the whole system, and all the signals are bounded in the closed-loop system. The intelligent position controller combines the merits of the adaptive fuzzy control with robust characteristics and learning ability for periodic command tracking of a servo drive mechanism. The simulation and experimental results on square, triangle, star, and circle reference contours are presented to show that the proposed controller indeed accomplishes the better tracking performances with regard to model uncertainties. It is observed that the convergence of parameters and tracking errors can be faster and smaller compared with the conventional adaptive fuzzy control in terms of average tracking error and tracking error standard deviation.

  15. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for ...

  16. Adaptation in the fuzzy self-organising controller

    DEFF Research Database (Denmark)

    Jantzen, Jan; Poulsen, Niels Kjølstad

    2003-01-01

    This simulation study provides an analysis of the adaptation mechanism in the self-organising fuzzy controller, SOC. The approach is to apply a traditional adaptive control viewpoint. A simplified performance measure in the SOC controller is used in a loss function, and thus the MIT rule implies...... an update mechanism similar to the SOC update mechanism. Two simulations of proportionally controlled systems show the behaviour of the proportional gain as it adapts to a specified behaviour....

  17. Analytic Model Predictive Control of Uncertain Nonlinear Systems: A Fuzzy Adaptive Approach

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    Xiuyan Peng

    2015-01-01

    Full Text Available A fuzzy adaptive analytic model predictive control method is proposed in this paper for a class of uncertain nonlinear systems. Specifically, invoking the standard results from the Moore-Penrose inverse of matrix, the unmatched problem which exists commonly in input and output dimensions of systems is firstly solved. Then, recurring to analytic model predictive control law, combined with fuzzy adaptive approach, the fuzzy adaptive predictive controller synthesis for the underlying systems is developed. To further reduce the impact of fuzzy approximation error on the system and improve the robustness of the system, the robust compensation term is introduced. It is shown that by applying the fuzzy adaptive analytic model predictive controller the rudder roll stabilization system is ultimately uniformly bounded stabilized in the H-infinity sense. Finally, simulation results demonstrate the effectiveness of the proposed method.

  18. Application of adaptive fuzzy control technology to pressure control of a pressurizer

    Institute of Scientific and Technical Information of China (English)

    YANG Ben-kun; BIAN Xin-qian; GUO Wei-lai

    2005-01-01

    A pressurizer is one of important equipment in a pressurized water reactor plant. It is used to maintain the pressure of primary coolant within allowed range because the sharp change of coolant pressure affects the security of reactor,therefor,the study of pressurizer's pressure control methods is very important. In this paper, an adaptive fuzzy controller is presented for pressure control of a pressurizer in a nuclear power plant. The controller can on-line tune fuzzy control rules and parameters by self-learning in the actual control process, which possesses the way of thinking like human to make a decision. The simulation results for a pressurized water reactor plant show that the adaptive fuzzy controller has optimum and intelligent characteristics, which prove the controller is effective.

  19. Evaluation-Function-based Model-free Adaptive Fuzzy Control

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    Agus Naba

    2016-12-01

    Full Text Available Designs of adaptive fuzzy controllers (AFC are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark verified the proposed scheme’s efficacy.

  20. Developed adaptive neuro-fuzzy algorithm to control air conditioning ...

    African Journals Online (AJOL)

    The paper developed artificial intelligence technique adaptive neuro-fuzzy controller for air conditioning systems at different pressures. The first order Sugeno fuzzy inference system was implemented and utilized for modeling and controller design. In addition, the estimation of the heat transfer rate and water mass flow rate ...

  1. Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System.

    Science.gov (United States)

    Wang, Ning; Er, Meng Joo; Sun, Jing-Chao; Liu, Yan-Cheng

    2016-07-01

    In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.

  2. Adaptive fuzzy sliding control of single-phase PV grid-connected inverter.

    Science.gov (United States)

    Fei, Juntao; Zhu, Yunkai

    2017-01-01

    In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance.

  3. Multi-Model Adaptive Fuzzy Controller for a CSTR Process

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    Shubham Gogoria

    2015-09-01

    Full Text Available Continuous Stirred Tank Reactors are intensively used to control exothermic reactions in chemical industries. It is a very complex multi-variable system with non-linear characteristics. This paper deals with linearization of the mathematical model of a CSTR Process. Multi model adaptive fuzzy controller has been designed to control the reactor concentration and temperature of CSTR process. This method combines the output of multiple Fuzzy controllers, which are operated at various operating points. The proposed solution is a straightforward implementation of Fuzzy controller with gain scheduler to control the linearly inseparable parameters of a highly non-linear process.

  4. Applications of Fuzzy adaptive PID control in the thermal power plant denitration liquid ammonia evaporation

    Directory of Open Access Journals (Sweden)

    Li Jing

    2016-01-01

    Full Text Available For the control of the liquid level of liquid ammonia in thermal power plant’s ammonia vaporization room, traditional PID controller parameter tuning is difficult to adapt to complex control systems, the setting of the traditional PID controller parameters is difficult to adapt to the complex control system. For the disadvantage of bad parameter setting, poor performance and so on the fuzzy adaptive PID control is proposed. Fuzzy adaptive PID control combines the advantages of traditional PID technology and fuzzy control. By using the fuzzy controller to intelligent control the object, the performance of the PID controller is further improved, and the control precision of the system is improved[1]. The simulation results show that the fuzzy adaptive PID controller not only has the advantages of high accuracy of PID controller, but also has the characteristics of fast and strong adaptability of fuzzy controller. It realizes the optimization of PID parameters which are in the optimal state, and the maximum increase production efficiency, so that are more suitable for nonlinear dynamic system.

  5. Incremental Adaptive Fuzzy Control for Sensorless Stroke Control of A Halbach-type Linear Oscillatory Motor

    Science.gov (United States)

    Lei, Meizhen; Wang, Liqiang

    2018-01-01

    The halbach-type linear oscillatory motor (HT-LOM) is multi-variable, highly coupled, nonlinear and uncertain, and difficult to get a satisfied result by conventional PID control. An incremental adaptive fuzzy controller (IAFC) for stroke tracking was presented, which combined the merits of PID control, the fuzzy inference mechanism and the adaptive algorithm. The integral-operation is added to the conventional fuzzy control algorithm. The fuzzy scale factor can be online tuned according to the load force and stroke command. The simulation results indicate that the proposed control scheme can achieve satisfied stroke tracking performance and is robust with respect to parameter variations and external disturbance.

  6. Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors

    Directory of Open Access Journals (Sweden)

    Yongqing Fan

    2018-01-01

    Full Text Available A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

  7. Adaptive control of discrete-time chaotic systems: a fuzzy control approach

    International Nuclear Information System (INIS)

    Feng Gang; Chen Guanrong

    2005-01-01

    This paper discusses adaptive control of a class of discrete-time chaotic systems from a fuzzy control approach. Using the T-S model of discrete-time chaotic systems, an adaptive control algorithm is developed based on some conventional adaptive control techniques. The resulting adaptively controlled chaotic system is shown to be globally stable, and its robustness is discussed. A simulation example of the chaotic Henon map control is finally presented, to illustrate an application and the performance of the proposed control algorithm

  8. Control of input delayed pneumatic vibration isolation table using adaptive fuzzy sliding mode

    Directory of Open Access Journals (Sweden)

    Mostafa Khazaee

    Full Text Available AbstractPneumatic isolators are promising candidates for increasing the quality of accurate instruments. For this purpose, higher performance of such isolators is a prerequisite. In particular, the time-delay due to the air transmission is an inherent issue with pneumatic systems, which needs to be overcome using modern control methods. In this paper an adaptive fuzzy sliding mode controller is proposed to improve the performance of a pneumatic isolator in the low frequency range, i.e., where the passive techniques have obvious shortcomings. The main idea is to combine the adaptive fuzzy controller with adaptive predictor as a new time delay control technique. The adaptive fuzzy sliding mode control and the adaptive fuzzy predictor help to circumvent the input delay and nonlinearities in such isolators. The main advantage of the proposed method is that the closed-loop system stability is guaranteed under certain conditions. Simulation results reveal the effectiveness of the proposed method, compared with other existing time -delay control methods.

  9. Adaptive Fuzzy Output-Feedback Method Applied to Fin Control for Time-Delay Ship Roll Stabilization

    Directory of Open Access Journals (Sweden)

    Rui Bai

    2014-01-01

    Full Text Available The ship roll stabilization by fin control system is considered in this paper. Assuming that angular velocity in roll cannot be measured, an adaptive fuzzy output-feedback control is investigated. The fuzzy logic system is used to approximate the uncertain term of the controlled system, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the fuzzy state observer and combining the adaptive backstepping technique with adaptive fuzzy control design, an observer-based adaptive fuzzy output-feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB, and the control strategy is effective to decrease the roll motion. Simulation results are included to illustrate the effectiveness of the proposed approach.

  10. Developed adaptive neuro-fuzzy algorithm to control air conditioning ...

    African Journals Online (AJOL)

    user

    The paper developed artificial intelligence technique adaptive neuro-fuzzy ... system is highly appreciated and essential in most of our daily life. ... It can construct an input-output mapping based on human knowledge and specific input-output data ... fuzzy controllers to produce desirable internal temperature and air quality, ...

  11. Design of sewage treatment system by applying fuzzy adaptive PID controller

    Science.gov (United States)

    Jin, Liang-Ping; Li, Hong-Chan

    2013-03-01

    In the sewage treatment system, the dissolved oxygen concentration control, due to its nonlinear, time-varying, large time delay and uncertainty, is difficult to establish the exact mathematical model. While the conventional PID controller only works with good linear not far from its operating point, it is difficult to realize the system control when the operating point far off. In order to solve the above problems, the paper proposed a method which combine fuzzy control with PID methods and designed a fuzzy adaptive PID controller based on S7-300 PLC .It employs fuzzy inference method to achieve the online tuning for PID parameters. The control algorithm by simulation and practical application show that the system has stronger robustness and better adaptability.

  12. Adaptive fuzzy control for a simulation of hydraulic analogy of a nuclear reactor

    International Nuclear Information System (INIS)

    Ruan, D.; Li, X.; Eynde, G. van den

    2000-01-01

    In the framework of the on-going R and D project on fuzzy control applications to the Belgian Reactor 1 (BR1) at the Belgian Nuclear Research Centre (SCK-CEN), we have constructed a real fuzzy-logic-control demo model. The demo model is suitable for us to test and compare some new algorithms of fuzzy control and intelligent systems, which is advantageous because it is always difficult and time consuming, due to safety aspects, to do all experiments in a real nuclear environment. In this chapter, we first report briefly on the construction of the demo model, and then introduce the results of a fuzzy control, a proportional-integral-derivative (PID) control and an advanced fuzzy control, in which the advanced fuzzy control is a fuzzy control with an adaptive function that can self-regulate the fuzzy control rules. Afterwards, we present a comparative study of those three methods. The results have shown that fuzzy control has more advantages in terms of flexibility, robustness, and easily updated facilities with respect to the PID control of the demo model, but that PID control has much higher regulation resolution due to its integration terms. The adaptive fuzzy control can dynamically adjust the rule base, therefore it is more robust and suitable to those very uncertain occasions. (orig.)

  13. Synchronization of discrete-time spatiotemporal chaos via adaptive fuzzy control

    International Nuclear Information System (INIS)

    Xue Yueju; Yang Shiyuan

    2003-01-01

    A discrete-time adaptive fuzzy control scheme is presented to synchronize model-unknown coupled Henon-map lattices (CHMLs). The proposed method is robust to approximate errors, parameter mismatches and disturbances, because it integrates the merits of the adaptive fuzzy systems and the variable structure control with a sector. The simulation results of synchronization of CHMLs show that it not only can synchronize model-unknown CHMLs but also is robust against parameter mismatches and noise of the systems. These merits are advantageous for engineering realization

  14. Synchronization of discrete-time spatiotemporal chaos via adaptive fuzzy control

    Energy Technology Data Exchange (ETDEWEB)

    Xue Yueju E-mail: xueyj@mail.tsinghua.edu.cn; Yang Shiyuan E-mail: ysy-dau@tsinghua.edu.cn

    2003-08-01

    A discrete-time adaptive fuzzy control scheme is presented to synchronize model-unknown coupled Henon-map lattices (CHMLs). The proposed method is robust to approximate errors, parameter mismatches and disturbances, because it integrates the merits of the adaptive fuzzy systems and the variable structure control with a sector. The simulation results of synchronization of CHMLs show that it not only can synchronize model-unknown CHMLs but also is robust against parameter mismatches and noise of the systems. These merits are advantageous for engineering realization.

  15. Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems

    Directory of Open Access Journals (Sweden)

    Lili Zhang

    2014-01-01

    Full Text Available A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach.

  16. Induction machine Direct Torque Control system based on fuzzy adaptive control

    Science.gov (United States)

    Li, Shi-ping; Yu, Yan; Jiao, Zhen-gang; Gu, Shu-sheng

    2009-07-01

    Direct Torque Control technology is a high-performance communication control method, it uses the space voltage vector method, and then to the inverter switch state control, to obtain high torque dynamic performance. But none of the switching states is able to generate the exact voltage vector to produce the desired changes in torque and flux in most of the switching instances. This causes a high ripple in torque. To solve this problem, a fuzzy implementation of Direct Torque Control of Induction machine is presented here. Error of stator flux, error of motor electromagnetic torque and position of angle of flux are taken as fuzzy variables. In order to further solve nonlinear problem of variation parameters in direct torque control system, the paper proposes a fuzzy parameter PID adaptive control method which is suitable for the direct torque control of an asynchronous motor. The generation of its fuzzy control is obtained by analyzing and optimizing PID control step response and combining expert's experience. For this reason, it carries out fuzzy work to PID regulator of motor speed to achieve to regulate PID parameters. Therefore the control system gets swifter response velocity, stronger robustness and higher precision of velocity control. The computer simulated results verify the validity of this novel method.

  17. Synchronization of generalized Henon map by using adaptive fuzzy controller

    Energy Technology Data Exchange (ETDEWEB)

    Xue Yueju E-mail: xueyj@mail.tsinghua.edu.cn; Yang Shiyuan E-mail: ysy-dau@tsinghua.edu.cn

    2003-08-01

    In this paper, an adaptive fuzzy control method is presented to synchronize model-unknown discrete-time generalized Henon map. The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptive fuzzy and the variable structure control. Moreover, it can realize the synchronizations of non-identical chaotic systems. The simulation results of synchronization of generalized Henon map show that it not only can synchronize model-unknown generalized Henon map but also is robust against the noise of the systems. These merits are advantageous for engineering realization.

  18. Synchronization of generalized Henon map by using adaptive fuzzy controller

    International Nuclear Information System (INIS)

    Xue Yueju; Yang Shiyuan

    2003-01-01

    In this paper, an adaptive fuzzy control method is presented to synchronize model-unknown discrete-time generalized Henon map. The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptive fuzzy and the variable structure control. Moreover, it can realize the synchronizations of non-identical chaotic systems. The simulation results of synchronization of generalized Henon map show that it not only can synchronize model-unknown generalized Henon map but also is robust against the noise of the systems. These merits are advantageous for engineering realization

  19. Interval type-2 fuzzy gain-adaptive controller of a Doubly Fed ...

    African Journals Online (AJOL)

    ... Interval Type-2 Fuzzy Gain Adaptive IP (IT2FGAIP) controller and a conventional IP controller ... and an adaptive IP controller is proposed for the speed control of DFIM in the presence of ... EMAIL FREE FULL TEXT EMAIL FREE FULL TEXT

  20. Stabilizing periodic orbits of chaotic systems using fuzzy adaptive sliding mode control

    Energy Technology Data Exchange (ETDEWEB)

    Layeghi, Hamed [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: layeghi@mech.sharif.edu; Arjmand, Mehdi Tabe [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: arjmand@mech.sharif.edu; Salarieh, Hassan [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Department of Mechanical Engineering, Sharif University of Technology, Center of Excellence in Design, Robotics and Automation, Azadi Avenue, Postal Code 11365-9567 Tehran (Iran, Islamic Republic of)], E-mail: aalasti@sharif.edu

    2008-08-15

    In this paper by using a combination of fuzzy identification and the sliding mode control a fuzzy adaptive sliding mode scheme is designed to stabilize the unstable periodic orbits of chaotic systems. The chaotic system is assumed to have an affine form x{sup (n)} = f(X) + g(X)u where f and g are unknown functions. Using only the input-output data obtained from the underlying dynamical system, two fuzzy systems are constructed for identification of f and g. Two distinct methods are utilized for fuzzy modeling, the least squares and the gradient descent techniques. Based on the estimated fuzzy models, an adaptive controller, which works through the sliding mode control, is designed to make the system track the desired unstable periodic orbits. The stability analysis of the overall closed loop system is presented in the paper and the effectiveness of the proposed adaptive scheme is numerically investigated. As a case of study, modified Duffing system is selected for applying the proposed method to stabilize its 2{pi} and 4{pi} periodic orbits. Simulation results show the high performance of the method for stabilizing the unstable periodic orbits of unknown chaotic systems.

  1. UAV Controller Based on Adaptive Neuro-Fuzzy Inference System and PID

    Directory of Open Access Journals (Sweden)

    Ali Moltajaei Farid

    2013-01-01

    Full Text Available ANFIS is combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system, capable of reasoning and learning in an uncertain and imprecise environment. In this paper, an adaptive neuro-fuzzy inference system (ANFIS is employed to control an unmanned aircraft vehicle (UAV.  First, autopilots structure is defined, and then ANFIS controller is applied, to control UAVs lateral position. The results of ANFIS and PID lateral controllers are compared, where it shows the two controllers have similar results. ANFIS controller is capable to adaptation in nonlinear conditions, while PID has to be tuned to preserves proper control in some conditions. The simulation results generated by Matlab using Aerosim Aeronautical Simulation Block Set, which provides a complete set of tools for development of six degree-of-freedom. Nonlinear Aerosonde unmanned aerial vehicle model with ANFIS controller is simulated to verify the capability of the system. Moreover, the results are validated by FlightGear flight simulator.

  2. Fuzzy Adaptive Model Following Speed Control for Vector Controlled Permanent Magnet Synchronous Motor

    Directory of Open Access Journals (Sweden)

    Baghdad BELABES

    2008-12-01

    Full Text Available In this paper a hybrid controller combining a linear model following controller (LMFC and fuzzy logic control (FLC for speed vector controlled permanent magnet synchronous motor (PMSM is described on this study. The FLC is introduced at the adaptive mechanism level. First, an LMFC system is designed to allow the plant states to be controlled to follow the states produced by a reference model. In the nominal conditions, the model following is perfect and the adaptive mechanism based on the fuzzy logic is idle. Secondly, when parameter variations or external disturbances occur, an augmented signal will be generated by FLC mechanism to preserve the desired model following control performance. The effectiveness and robustness of the proposed controller is demonstrated by some simulation results.

  3. Adaptive Interval Type-2 Fuzzy Logic Control for PMSM Drives with a Modified Reference Frame

    KAUST Repository

    Chaoui, Hicham; Khayamy, Mehdy; Aljarboua, Abdullah Abdulaziz

    2017-01-01

    In this paper, an adaptive interval type-2 fuzzy logic control scheme is proposed for high-performance permanent magnet synchronous machine drives. This strategy combines the power of type-2 fuzzy logic systems with the adaptive control theory

  4. A neural fuzzy controller learning by fuzzy error propagation

    Science.gov (United States)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  5. Design of a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological damper

    International Nuclear Information System (INIS)

    Phu, Do Xuan; Shah, Kruti; Choi, Seung-Bok

    2014-01-01

    This paper presents a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological (MR) fluid damper in order to validate the effectiveness of the control performance. An interval type 2 fuzzy model is built, and then combined with modified adaptive control to achieve the desired damping force. In the formulation of the new adaptive controller, an enhanced iterative algorithm is integrated with the fuzzy model to decrease the time of calculation (D Wu 2013 IEEE Trans. Fuzzy Syst. 21 80–99) and the control algorithm is synthesized based on the H ∞ tracking technique. In addition, for the verification of good control performance of the proposed controller, a cylindrical MR damper which can be applied to the vibration control of a washing machine is designed and manufactured. For the operating fluid, a recently developed plate-like particle-based MR fluid is used instead of a conventional MR fluid featuring spherical particles. To highlight the control performance of the proposed controller, two existing adaptive fuzzy control algorithms proposed by other researchers are adopted and altered for a comparative study. It is demonstrated from both simulation and experiment that the proposed new adaptive controller shows better performance of damping force control in terms of response time and tracking accuracy than the existing approaches. (papers)

  6. Design and FPGA-implementation of an improved adaptive fuzzy logic controller for DC motor speed control

    Directory of Open Access Journals (Sweden)

    E.A. Ramadan

    2014-09-01

    Full Text Available This paper presents an improved adaptive fuzzy logic speed controller for a DC motor, based on field programmable gate array (FPGA hardware implementation. The developed controller includes an adaptive fuzzy logic control (AFLC algorithm, which is designed and verified with a nonlinear model of DC motor. Then, it has been synthesised, functionally verified and implemented using Xilinx Integrated Software Environment (ISE and Spartan-3E FPGA. The performance of this controller has been successfully validated with good tracking results under different operating conditions.

  7. Robust Longitudinal Aircraft- Control Based on an Adaptive Fuzzy-Logic Algorithm

    Directory of Open Access Journals (Sweden)

    Abdel- Latif Elshafei

    2002-06-01

    Full Text Available To study the aircraft response to a fast pull-up manoeuvre, a short period approximation of the longitudinal model is considered. The model is highly nonlinear and includes parametric uncertainties. To cope with a wide range of command signals, a robust adaptive fuzzy logic controller is proposed. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, robustifying and adaptive components are included in the control law to compensate for modeling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the system’s nonlinearity. The second system is an adaptive one that compensates for modeling errors. The derivation of the control law based on a dynamic game approach is given in detail. Stability of the closed-loop control system is also verified. Simulation results based on an F16-model illustrate a successful tracking performance of the proposed controller.

  8. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1997-12-01

    This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

  9. Tracking Control of a 2-DOF Arm Actuated by Pneumatic Muscle Actuators Using Adaptive Fuzzy Sliding Mode Control

    Science.gov (United States)

    Chang, Ming-Kun; Wu, Jui-Chi

    Pneumatic muscle actuators (PMAs) have the highest power/weight ratio and power/volume ratio of any actuator. Therefore, they can be used not only in the rehabilitation engineering, but also as an actuator in robots, including industrial robots and therapy robots. It is difficult to achieve excellent tracking performance using classical control methods because the compressibility of gas and the nonlinear elasticity of bladder container causes parameter variations. An adaptive fuzzy sliding mode control is developed in this study. The fuzzy sliding surface can be used to reduce fuzzy rule numbers, and the adaptive control law is used to modify fuzzy rules on-line. A model matching technique is then adopted to adjust scaling factors. The experimental results show that this control strategy can attain excellent tracking performance.

  10. Adaptive Fuzzy Robust Control for a Class of Nonlinear Systems via Small Gain Theorem

    Directory of Open Access Journals (Sweden)

    Xingjian Wang

    2013-01-01

    Full Text Available Practical nonlinear systems can usually be represented by partly linearizable models with unknown nonlinearities and external disturbances. Based on this consideration, we propose a novel adaptive fuzzy robust control (AFRC algorithm for such systems. The AFRC effectively combines techniques of adaptive control and fuzzy control, and it improves the performance by retaining the advantages of both methods. The linearizable part will be linearly parameterized with unknown but constant parameters, and the discontinuous-projection-based adaptive control law is used to compensate these parts. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown nonlinearities. Robust control law ensures the robustness of closed-loop control system. A systematic design procedure of the AFRC algorithm by combining the backstepping technique and small-gain approach is presented. Then the closed-loop stability is studied by using small gain theorem, and the result indicates that the closed-loop system is semiglobally uniformly ultimately bounded.

  11. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is ... The robust controller is used to guarantee the stability and to control the per- ..... From the above analysis we have the following theorem:.

  12. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    Science.gov (United States)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  13. Adaptive Fuzzy Output-Constrained Fault-Tolerant Control of Nonlinear Stochastic Large-Scale Systems With Actuator Faults.

    Science.gov (United States)

    Li, Yongming; Ma, Zhiyao; Tong, Shaocheng

    2017-09-01

    The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

  14. Design of a self-adaptive fuzzy PID controller for piezoelectric ceramics micro-displacement system

    Science.gov (United States)

    Zhang, Shuang; Zhong, Yuning; Xu, Zhongbao

    2008-12-01

    In order to improve control precision of the piezoelectric ceramics (PZT) micro-displacement system, a self-adaptive fuzzy Proportional Integration Differential (PID) controller is designed based on the traditional digital PID controller combining with fuzzy control. The arithmetic gives a fuzzy control rule table with the fuzzy control rule and fuzzy reasoning, through this table, the PID parameters can be adjusted online in real time control. Furthermore, the automatic selective control is achieved according to the change of the error. The controller combines the good dynamic capability of the fuzzy control and the high stable precision of the PID control, adopts the method of using fuzzy control and PID control in different segments of time. In the initial and middle stage of the transition process of system, that is, when the error is larger than the value, fuzzy control is used to adjust control variable. It makes full use of the fast response of the fuzzy control. And when the error is smaller than the value, the system is about to be in the steady state, PID control is adopted to eliminate static error. The problems of PZT existing in the field of precise positioning are overcome. The results of the experiments prove that the project is correct and practicable.

  15. Novel Fuzzy-Modeling-Based Adaptive Synchronization of Nonlinear Dynamic Systems

    Directory of Open Access Journals (Sweden)

    Shih-Yu Li

    2017-01-01

    Full Text Available In this paper, a novel fuzzy-model-based adaptive synchronization scheme and its fuzzy update laws of parameters are proposed to address the adaptive synchronization problem. The proposed fuzzy controller does not share the same premise of fuzzy system, and the numbers of fuzzy controllers is reduced effectively through the novel modeling strategy. In addition, based on the adaptive synchronization scheme, the error dynamic system can be guaranteed to be asymptotically stable and the true values of unknown parameters can be obtained. Two identical complicated dynamic systems, Mathieu-Van der pol system (M-V system with uncertainties, are illustrated for numerical simulation example to show the effectiveness and feasibility of the proposed novel adaptive control strategy.

  16. Fuzzy systems for process identification and control

    International Nuclear Information System (INIS)

    Gorrini, V.; Bersini, H.

    1994-01-01

    Various issues related to the automatic construction and on-line adaptation of fuzzy controllers are addressed. A Direct Adaptive Fuzzy Control (this is an adaptive control methodology requiring a minimal knowledge of the processes to be coupled with) derived in a way reminiscent of neurocontrol methods, is presented. A classical fuzzy controller and a fuzzy realization of a PID controller is discussed. These systems implement a highly non-linear control law, and provide to be quite robust, even in the case of noisy inputs. In order to identify dynamic processes of order superior to one, we introduce a more complex architecture, called Recurrent Fuzzy System, that use some fuzzy internal variables to perform an inferential chaining.I

  17. Control of suspended low-gravity simulation system based on self-adaptive fuzzy PID

    Science.gov (United States)

    Chen, Zhigang; Qu, Jiangang

    2017-09-01

    In this paper, an active suspended low-gravity simulation system is proposed to follow the vertical motion of the spacecraft. Firstly, working principle and mathematical model of the low-gravity simulation system are shown. In order to establish the balance process and suppress the strong position interference of the system, the idea of self-adaptive fuzzy PID control strategy is proposed. It combines the PID controller with a fuzzy controll strategy, the control system can be automatically adjusted by changing the proportional parameter, integral parameter and differential parameter of the controller in real-time. At last, we use the Simulink tools to verify the performance of the controller. The results show that the system can reach balanced state quickly without overshoot and oscillation by the method of the self-adaptive fuzzy PID, and follow the speed of 3m/s, while simulation degree of accuracy of system can reach to 95.9% or more.

  18. Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

    Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

  19. Model-Based Evolution of a Fast Hybrid Fuzzy Adaptive Controller for a Pneumatic Muscle Actuator

    Directory of Open Access Journals (Sweden)

    Alexander Hošovský

    2012-07-01

    Full Text Available Pneumatic artificial muscle-based robotic systems usually necessitate the use of various nonlinear control techniques in order to improve their performance. Their robustness to parameter variation, which is generally difficult to predict, should also be tested. Here a fast hybrid adaptive control is proposed, where a conventional PD controller is placed into the feedforward branch and a fuzzy controller is placed into the adaptation branch. The fuzzy controller compensates for the actions of the PD controller under conditions of inertia moment variation. The fuzzy controller of Takagi-Sugeno type is evolved through a genetic algorithm using the dynamic model of a pneumatic muscle actuator. The results confirm the capability of the designed system to provide robust performance under the conditions of varying inertia.

  20. Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Tat-Bao-Thien Nguyen

    2014-01-01

    Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.

  1. Vibrations control of light rail transportation vehicle via PID type fuzzy controller using parameters adaptive method

    OpenAIRE

    METİN, Muzaffer; GÜÇLÜ, Rahmi

    2014-01-01

    In this study, a conventional PID type fuzzy controller and parameter adaptive fuzzy controller are designed to control vibrations actively of a light rail transport vehicle which modeled as 6 degree-of-freedom system and compared performances of these two controllers. Rail vehicle model consists of a passenger seat and its suspension system, vehicle body, bogie, primary and secondary suspensions and wheels. The similarity between mathematical model and real system is shown by compar...

  2. Adaptive Controller for 6-DOF Parallel Robot Using T-S Fuzzy Inference

    Directory of Open Access Journals (Sweden)

    Xue Jian

    2013-02-01

    Full Text Available 6-DOF parallel robot always appears in the form of Stewart platform. It has been widely used in industry for the benefits such as strong structural stiffness, high movement accuracy and so on. Space docking technology makes higher requirements of motion accuracy and dynamic performance to the control method on 6-DOF parallel robot. In this paper, a hydraulic 6-DOF parallel robot was used to simulate the docking process. Based on this point, this paper gave a thorough study on the design of an adaptive controller to eliminate the asymmetric of controlled plant and uncertain load force interference. Takagi-Sugeno (T-S fuzzy inference model was used to build the fuzzy adaptive controller. With T-S model, the controller directly imposes adaptive control signal on the plant to make sure that the output of plant could track the reference model output. The controller has simple structure and is easy to implement. Experiment results show that the controller can eliminate asymmetric and achieve good dynamic performance, and has good robustness to load interference.

  3. Speed control of permanent magnet excitation transverse flux linear motor by using adaptive neuro-fuzzy controller

    Energy Technology Data Exchange (ETDEWEB)

    Hasanien, Hany M., E-mail: Hanyhasanien@ieee.or [Dept. of Elec. Power and Machines, Faculty of Eng., Ain-shams Univ. Cairo (Egypt); Muyeen, S.M. [Department of Electrical Engineering, Petroleum Institute, Abu Dhabi (United Arab Emirates); Tamura, Junji [Department of EEE, Kitami Institute of Technology, 165 Koen Cho, Kitami 090-8507, Hokkaido (Japan)

    2010-12-15

    This paper presents a novel adaptive neuro-fuzzy controller applies on transverse flux linear motor for controlling its speed. The proposed controller presents fuzzy logic controller with self tuning scaling factors based on artificial neural network structure. It has two input variables and one control output variable. Firstly the fuzzy logic control rules are described then NN architecture is represented to self tune the output scaling factors of the controller. The application of this control technique represents the novelty of work, where this algorithm has so far not been stated before for this type of drives. This methodology solves the problem of nonlinearities and load changes of TFLM drives. The dynamic response of the motor is studied under the rated load condition as well as load disturbances. The proposed controller ensures fast and accurate dynamic response with an excellent steady state performance. The dynamic response of the motor with the proposed controller is compared with PI and adaptive NN controllers. It is found that the proposed controller gives better and faster response from the viewpoint of overshoot and settling time. Matlab/Simulink tool is used for this dynamic simulation study.

  4. dSPACE based adaptive neuro-fuzzy controller of grid interactive inverter

    International Nuclear Information System (INIS)

    Altin, Necmi; Sefa, İbrahim

    2012-01-01

    Highlights: ► We propose a dSPACE based neuro-fuzzy controlled grid interactive inverter. ► The membership functions and rule base of fuzzy logic controller by using ANFIS. ► A LCL output filter is designed. ► A high performance controller is designed. - Abstract: In this study, design, simulation and implementation of a dSPACE based grid interactive voltage source inverter are proposed. This inverter has adaptive neuro-fuzzy controller and capable of importing electrical energy, generated from renewable energy sources such as the wind, the solar and the fuel cells to the grid. A line frequency transformer and a LCL filter are used at the output of the grid interactive inverter which is designed as current controlled to decrease the susceptibility to phase errors. Membership functions and rule base of the fuzzy logic controller, which control the inverter output current, are determined by using artificial neural networks. Both simulation and experimental results show that, the grid interactive inverter operates synchronously with the grid. The inverter output current which is imported to the grid is in sinusoidal waveform and the harmonic level of it meets the international standards (4.3 < 5.0%). In addition, simulation and experimental results of the neuro-fuzzy and the PI controlled inverter are given together and compared in detail. Simulation and experimental results show that the proposed inverter has faster response to the reference variations and lower steady state error than PI controller.

  5. Fuzzy control and identification

    CERN Document Server

    Lilly, John H

    2010-01-01

    This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.

  6. Design and simplification of Adaptive Neuro-Fuzzy Inference Controllers for power plants

    Energy Technology Data Exchange (ETDEWEB)

    Alturki, F.A.; Abdennour, A. [King Saud University, Riyadh (Saudi Arabia). Electrical Engineering Dept.

    1999-10-01

    This article presents the design of an Adaptive Neuro-Fuzzy Inference Controller (ANFIC) for a 160 MW power plant. The space of operating conditions of the plant is partitioned into five regions. For each of the regions, an optimal controller is designed to meet a set of design objectives. The resulting five linear controllers are used to train the ANFIC. To enhance the applicability of the control system, a new algorithm that reduces the fuzzy rules to the most essential ones is also presented. This algorithm offers substantial savings in computation time while maintaining the performance and robustness of the original controller. (author)

  7. Foundations Of Fuzzy Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well...... as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID...

  8. Fuzzy Adaptive Output Feedback Control of Uncertain Nonlinear Systems With Prescribed Performance.

    Science.gov (United States)

    Zhang, Jin-Xi; Yang, Guang-Hong

    2018-05-01

    This paper investigates the tracking control problem for a family of strict-feedback systems in the presence of unknown nonlinearities and immeasurable system states. A low-complexity adaptive fuzzy output feedback control scheme is proposed, based on a backstepping method. In the control design, a fuzzy adaptive state observer is first employed to estimate the unmeasured states. Then, a novel error transformation approach together with a new modification mechanism is introduced to guarantee the finite-time convergence of the output error to a predefined region and ensure the closed-loop stability. Compared with the existing methods, the main advantages of our approach are that: 1) without using extra command filters or auxiliary dynamic surface control techniques, the problem of explosion of complexity can still be addressed and 2) the design procedures are independent of the initial conditions. Finally, two practical examples are performed to further illustrate the above theoretic findings.

  9. Fuzzy adaptive robust control for space robot considering the effect of the gravity

    Directory of Open Access Journals (Sweden)

    Qin Li

    2014-12-01

    Full Text Available Space robot is assembled and tested in gravity environment, and completes on-orbit service (OOS in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of gravity in different working condition. Fully considering the change of kinematic and dynamic models caused by the change of gravity environment, a fuzzy adaptive robust control (FARC strategy which is adaptive to these model variations is put forward for trajectory tracking control of space robot. A fuzzy algorithm is employed to approximate the nonlinear uncertainties in the model, adaptive laws of the parameters are constructed, and the approximation error is compensated by using a robust control algorithm. The stability of the control system is guaranteed based on the Lyapunov theory and the trajectory tracking control simulation is performed. The simulation results are compared with the proportional plus derivative (PD controller, and the effectiveness to achieve better trajectory tracking performance under different gravity environment without changing the control parameters and the advantage of the proposed controller are verified.

  10. Quasi-adaptive fuzzy heating control of solar buildings

    Energy Technology Data Exchange (ETDEWEB)

    Gouda, M.M. [Faculty of Industrial Education, Cairo (Egypt); Danaher, S. [University of Northumbria, Newcastle upon Tyne, (United Kingdom). School of Engineering; Underwood, C.P. [University of Northumbria, Newcastle upon Tyne (United Kingdom). School of Built Environment and Sustainable Cities Research Institute

    2006-12-15

    Significant progress has been made on maximising passive solar heat gains to building spaces in winter. Control of the space heating in these applications is complicated due to the lagging influence of the useful solar heat gain coupled with the wide range of construction materials and heating system choices. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set-up and commissioning procedures. This paper addresses the development and testing of a quasi-adaptive fuzzy logic control method that addresses these issues. The controller is developed in two steps. A feed-forward neural network is used to predict the internal air temperature, in which a singular value decomposition (SVD) algorithm is used to remove the highly correlated data from the inputs of the neural network to reduce the network structure. The fuzzy controller is then designed to have two inputs: the first input being the error between the set-point temperature and the internal air temperature and the second the predicted future internal air temperature. The controller was implemented in real-time using a test cell with controlled ventilation and a modulating electric heating system. Results, compared with validated simulations of conventionally controlled heating, confirm that the proposed controller achieves superior tracking and reduced overheating when compared with the conventional method of control. (author)

  11. Distributed Adaptive Fuzzy Control for Nonlinear Multiagent Systems Via Sliding Mode Observers.

    Science.gov (United States)

    Shen, Qikun; Shi, Peng; Shi, Yan

    2016-12-01

    In this paper, the problem of distributed adaptive fuzzy control is investigated for high-order uncertain nonlinear multiagent systems on directed graph with a fixed topology. It is assumed that only the outputs of each follower and its neighbors are available in the design of its distributed controllers. Equivalent output injection sliding mode observers are proposed for each follower to estimate the states of itself and its neighbors, and an observer-based distributed adaptive controller is designed for each follower to guarantee that it asymptotically synchronizes to a leader with tracking errors being semi-globally uniform ultimate bounded, in which fuzzy logic systems are utilized to approximate unknown functions. Based on algebraic graph theory and Lyapunov function approach, using Filippov-framework, the closed-loop system stability analysis is conducted. Finally, numerical simulations are provided to illustrate the effectiveness and potential of the developed design techniques.

  12. Neuro-fuzzy Control of Integrating Processes

    Directory of Open Access Journals (Sweden)

    Anna Vasičkaninová

    2011-11-01

    Full Text Available Fuzzy technology is adaptive and easily applicable in different areas.Fuzzy logic provides powerful tools to capture the perceptionof natural phenomena. The paper deals with tuning of neuro-fuzzy controllers for integrating plant and for integrating plantswith time delay. The designed approach is verified on three examples by simulations and compared plants with classical PID control.Designed fuzzy controllers lead to better closed-loop control responses then classical PID controllers.

  13. Adaptive fuzzy wavelet network control of second order multi-agent systems with unknown nonlinear dynamics.

    Science.gov (United States)

    Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam

    2017-07-01

    In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Intelligent neural network and fuzzy logic control of industrial and power systems

    Science.gov (United States)

    Kuljaca, Ognjen

    The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

  15. Design of Immune-Algorithm-Based Adaptive Fuzzy Controllers for Active Suspension Systems

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-04-01

    Full Text Available The aim of this paper is to integrate the artificial immune systems and adaptive fuzzy control for the automobile suspension system, which is regarded as a multiobjective optimization problem. Moreover, the fuzzy control rules and membership controls are then introduced for identification and memorization. It leads fast convergence in the search process. Afterwards, by using the diversity of the antibody group, trapping into local optimum can be avoided, and the system possesses a global search capacity and a faster local search for finding a global optimal solution. Experimental results show that the artificial immune system with the recognition and memory functions allows the system to rapidly converge and search for the global optimal approximate solutions.

  16. Adaptive Fuzzy Logic based MPPT Control for PV System Under Partial Shading Condition

    OpenAIRE

    Choudhury, Subhashree; Rout, Pravat Kumar

    2016-01-01

    Partial shading causes power loss, hotspots and threatens the reliability of the Photovoltaic generation system. Moreover characteristic curves exhibit multiple peaks. Conventional MPPT techniques under this condition often fail to give optimum MPP. Focusing on the afore mentioned problem an attempt has been made to design an Adaptive Takagi-Sugeno Fuzzy Inference System based Fuzzy Logic Control MPPT.The mathematical model of PV array is simulated using in MATLAB/Simulink environment.Various...

  17. Design of uav robust autopilot based on adaptive neuro-fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Mohand Achour Touat

    2008-04-01

    Full Text Available  This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.

  18. Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Doaa M. Atia

    2017-05-01

    Full Text Available The greenhouse is a complicated nonlinear system, which provides the plants with appropriate environmental conditions for growing. This paper presents a design of a control system for a greenhouse using geothermal energy as a power source for heating system. The greenhouse climate control problem is to create a favourable environment for the crop in order to reach predetermined results for high yield, high quality and low costs. Four controller techniques; PI control, fuzzy logic control, artificial neural network control and adaptive neuro-fuzzy control are used to adjust the greenhouse indoor temperature at the required value. MATLAB/SIMULINK is used to simulate the different types of controller techniques. Finally a comparative study between different control strategies is carried out.

  19. reactor power control using fuzzy logic

    International Nuclear Information System (INIS)

    Ahmed, A.E.E.

    2001-01-01

    power stabilization is a critical issue in nuclear reactors. convention pd- controller is currently used in egypt second testing research reactor (ETRR-2). two fuzzy controllers are proposed to control the reactor power of ETRR-2 reactor. the design of the first one is based on a set of linguistic rules that were adopted from the human operators experience. after off-line fuzzy computations, the controller is a lookup table, and thus, real time controller is achieved. comparing this f lc response with the pd-controller response, which already exists in the system, through studying the expected transients during the normal operation of ETRR-2 reactor, the simulation results show that, fl s has the better response, the second controller is adaptive fuzzy controller, which is proposed to deal with system non-linearity . The simulation results show that the proposed adaptive fuzzy controller gives a better integral square error (i se) index than the existing conventional od controller

  20. Direct Adaptive Tracking Control for a Class of Pure-Feedback Stochastic Nonlinear Systems Based on Fuzzy-Approximation

    Directory of Open Access Journals (Sweden)

    Huanqing Wang

    2014-01-01

    Full Text Available The problem of fuzzy-based direct adaptive tracking control is considered for a class of pure-feedback stochastic nonlinear systems. During the controller design, fuzzy logic systems are used to approximate the packaged unknown nonlinearities, and then a novel direct adaptive controller is constructed via backstepping technique. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood around the origin in the sense of mean quartic value. The main advantages lie in that the proposed controller structure is simpler and only one adaptive parameter needs to be updated online. Simulation results are used to illustrate the effectiveness of the proposed approach.

  1. Design and implementation of an adaptive critic-based neuro-fuzzy controller on an unmanned bicycle

    OpenAIRE

    Shafiekhani, Ali; Mahjoob, Mohammad J.; Akraminia, Mehdi

    2017-01-01

    Fuzzy critic-based learning forms a reinforcement learning method based on dynamic programming. In this paper, an adaptive critic-based neuro-fuzzy system is presented for an unmanned bicycle. The only information available for the critic agent is the system feedback which is interpreted as the last action performed by the controller in the previous state. The signal produced by the critic agent is used along with the error back propagation to tune (online) conclusion parts of the fuzzy infer...

  2. New fuzzy approximate model for indirect adaptive control of distributed solar collectors

    KAUST Repository

    Elmetennani, Shahrazed

    2014-06-01

    This paper studies the problem of controlling a parabolic solar collectors, which consists of forcing the outlet oil temperature to track a set reference despite possible environmental disturbances. An approximate model is proposed to simplify the controller design. The presented controller is an indirect adaptive law designed on the fuzzy model with soft-sensing of the solar irradiance intensity. The proposed approximate model allows the achievement of a simple low dimensional set of nonlinear ordinary differential equations that reproduces the dynamical behavior of the system taking into account its infinite dimension. Stability of the closed loop system is ensured by resorting to Lyapunov Control functions for an indirect adaptive controller.

  3. New fuzzy approximate model for indirect adaptive control of distributed solar collectors

    KAUST Repository

    Elmetennani, Shahrazed; Laleg-Kirati, Taous-Meriem

    2014-01-01

    This paper studies the problem of controlling a parabolic solar collectors, which consists of forcing the outlet oil temperature to track a set reference despite possible environmental disturbances. An approximate model is proposed to simplify the controller design. The presented controller is an indirect adaptive law designed on the fuzzy model with soft-sensing of the solar irradiance intensity. The proposed approximate model allows the achievement of a simple low dimensional set of nonlinear ordinary differential equations that reproduces the dynamical behavior of the system taking into account its infinite dimension. Stability of the closed loop system is ensured by resorting to Lyapunov Control functions for an indirect adaptive controller.

  4. Adaptive fuzzy PID control of hydraulic servo control system for large axial flow compressor

    Science.gov (United States)

    Wang, Yannian; Wu, Peizhi; Liu, Chengtao

    2017-09-01

    To improve the stability of the large axial compressor, an efficient and special intelligent hydraulic servo control system is designed and implemented. The adaptive fuzzy PID control algorithm is used to control the position of the hydraulic servo cylinder steadily, which overcomes the drawback that the PID parameters should be adjusted based on the different applications. The simulation and the test results show that the system has a better dynamic property and a stable state performance.

  5. Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks

    Science.gov (United States)

    Wu, Zhengping; Wu, Hao

    With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.

  6. An adaptive neuro-fuzzy controller for mold level control in continuous casting

    International Nuclear Information System (INIS)

    Zolghadri Jahromi, M.; Abolhassan Tash, F.

    2001-01-01

    Mold variations in continuous casting are believed to be the main cause of surface defects in the final product. Although a Pid controller is well capable of controlling the level under normal conditions, it cannot prevent large variations of mold level when a disturbance occurs in the form of nozzle unclogging. In this paper, dual controller architecture is presented, a Pid controller is used as the main controller of the plant and an adaptive neuro-fuzzy controller is used as an auxiliary controller to help the Pid during disturbed phases. The control is passed back to the Pid controller after the disturbance is being dealt with. Simulation results prove the effectiveness of this control strategy in reducing mold level variations during the unclogging period

  7. FUZZY LOGIC BASED ADAPTATION MECHANISM FOR ADAPTIVE LUENBERGER OBSERVER SENSORLESS DIRECT TORQUE CONTROL OF INDUCTION MOTOR

    Directory of Open Access Journals (Sweden)

    A. BENNASSAR

    2016-01-01

    Full Text Available Many industrial applications require high performance speed sensorless operation and demand new control methods in order to obtain fast dynamic response and insensitive to external disturbances. The current research aims to present the performance of the sensorless direct torque control (DTC of an induction motor (IM using adaptive Luenberger observer (ALO with fuzzy logic controller (FLC for adaptation mechanism. The rotor speed is regulated by proportional integral (PI anti-windup controller. The proposed strategy is directed to reduce the ripple on the torque and the flux. Numerical simulation results show the good performance and effectiveness of the proposed sensorless control for different references of the speed even both low and high speeds.

  8. Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System

    Science.gov (United States)

    Huang, Shiuh-Jer; Chen, Hung-Yi

    In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.

  9. System identification and adaptive control theory and applications of the neurofuzzy and fuzzy cognitive network models

    CERN Document Server

    Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A

    2014-01-01

    Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.  Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model  stems  from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...

  10. Adaptive Jacobian Fuzzy Attitude Control for Flexible Spacecraft Combined Attitude and Sun Tracking System

    Science.gov (United States)

    Chak, Yew-Chung; Varatharajoo, Renuganth

    2016-07-01

    Many spacecraft attitude control systems today use reaction wheels to deliver precise torques to achieve three-axis attitude stabilization. However, irrecoverable mechanical failure of reaction wheels could potentially lead to mission interruption or total loss. The electrically-powered Solar Array Drive Assemblies (SADA) are usually installed in the pitch axis which rotate the solar arrays to track the Sun, can produce torques to compensate for the pitch-axis wheel failure. In addition, the attitude control of a flexible spacecraft poses a difficult problem. These difficulties include the strong nonlinear coupled dynamics between the rigid hub and flexible solar arrays, and the imprecisely known system parameters, such as inertia matrix, damping ratios, and flexible mode frequencies. In order to overcome these drawbacks, the adaptive Jacobian tracking fuzzy control is proposed for the combined attitude and sun-tracking control problem of a flexible spacecraft during attitude maneuvers in this work. For the adaptation of kinematic and dynamic uncertainties, the proposed scheme uses an adaptive sliding vector based on estimated attitude velocity via approximate Jacobian matrix. The unknown nonlinearities are approximated by deriving the fuzzy models with a set of linguistic If-Then rules using the idea of sector nonlinearity and local approximation in fuzzy partition spaces. The uncertain parameters of the estimated nonlinearities and the Jacobian matrix are being adjusted online by an adaptive law to realize feedback control. The attitude of the spacecraft can be directly controlled with the Jacobian feedback control when the attitude pointing trajectory is designed with respect to the spacecraft coordinate frame itself. A significant feature of this work is that the proposed adaptive Jacobian tracking scheme will result in not only the convergence of angular position and angular velocity tracking errors, but also the convergence of estimated angular velocity to

  11. Preliminary Test of Adaptive Neuro-Fuzzy Inference System Controller for Spacecraft Attitude Control

    Directory of Open Access Journals (Sweden)

    Sung-Woo Kim

    2012-12-01

    Full Text Available The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS. An ANFIS produces a control signal for one of the three axes of a spacecraft’s body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

  12. Adaptive Interval Type-2 Fuzzy Logic Control for PMSM Drives with a Modified Reference Frame

    KAUST Repository

    Chaoui, Hicham

    2017-01-10

    In this paper, an adaptive interval type-2 fuzzy logic control scheme is proposed for high-performance permanent magnet synchronous machine drives. This strategy combines the power of type-2 fuzzy logic systems with the adaptive control theory to achieve accurate tracking and robustness to higher uncertainties. Unlike other controllers, the proposed strategy does not require electrical transducers and hence, no explicit currents loop regulation is needed, which yields a simplified control scheme. But, this limits the machine\\'s operation range since it results in a higher energy consumption. Therefore, a modified reference frame is also proposed in this paper to decrease the machine\\'s consumption. To better assess the performance of the new reference frame, comparison against its original counterpart is carried-out under the same conditions. Moreover, the stability of the closed-loop control scheme is guaranteed by a Lyapunov theorem. Simulation and experimental results for numerous situations highlight the effectiveness of the proposed controller in standstill, transient, and steady-state conditions.

  13. Design of a new adaptive fuzzy controller and its application to vibration control of a vehicle seat installed with an MR damper

    International Nuclear Information System (INIS)

    Phu, Do Xuan; Shin, Do Kyun; Choi, Seung-Bok

    2015-01-01

    This paper presents a new adaptive fuzzy controller featuring a combination of two different control methodologies: H infinity control technique and sliding mode control. It is known that both controllers are powerful in terms of high performance and robust stability. However, both control methods require an accurate dynamic model to design a state variable based controller in order to maintain their advantages. Thus, in this work a fuzzy control method which does not require an accurate dynamic model is adopted and two control methodologies are integrated to maintain the advantages even in an uncertain environment of the dynamic system. After a brief explanation of the interval type 2 fuzzy logic, a new adaptive fuzzy controller associated with the H infinity control and sliding mode control is formulated on the basis of Lyapunov stability theory. Subsequently, the formulated controller is applied to vibration control of a vehicle seat equipped with magnetorheological fluid damper (MR damper in short). An experimental setup for realization of the proposed controller is established and vibration control performances such as acceleration at the driver’s seat are evaluated. In addition, in order to demonstrate the effectiveness of the proposed controller, a comparative work with two existing controllers is undertaken. It is shown through simulation and experiment that the proposed controller can provide much better vibration control performance than the two existing controllers. (paper)

  14. Adaptive Fractional Fuzzy Sliding Mode Control for Multivariable Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Junhai Luo

    2014-01-01

    Full Text Available This paper presents a robust adaptive fuzzy sliding mode control method for a class of uncertain nonlinear systems. The fractional order calculus is employed in the parameter updating stage. The underlying stability analysis as well as parameter update law design is carried out by Lyapunov based technique. In the simulation, two examples including a comparison with the traditional integer order counterpart are given to show the effectiveness of the proposed method. The main contribution of this paper consists in the control performance is better for the fractional order updating law than that of traditional integer order.

  15. Control of a mechanical gripper with a fuzzy controller

    International Nuclear Information System (INIS)

    Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.

    1995-01-01

    A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers. (Author)

  16. An Adaptive Supervisory Sliding Fuzzy Cerebellar Model Articulation Controller for Sensorless Vector-Controlled Induction Motor Drive Systems

    Directory of Open Access Journals (Sweden)

    Shun-Yuan Wang

    2015-03-01

    Full Text Available This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC in the speed sensorless vector control of an induction motor (IM drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes.

  17. Fuzzy logic applications to control engineering

    Science.gov (United States)

    Langari, Reza

    1993-12-01

    This paper presents the results of a project presently under way at Texas A&M which focuses on the use of fuzzy logic in integrated control of manufacturing systems. The specific problems investigated here include diagnosis of critical tool wear in machining of metals via a neuro-fuzzy algorithm, as well as compensation of friction in mechanical positioning systems via an adaptive fuzzy logic algorithm. The results indicate that fuzzy logic in conjunction with conventional algorithmic based approaches or neural nets can prove useful in dealing with the intricacies of control/monitoring of manufacturing systems and can potentially play an active role in multi-modal integrated control systems of the future.

  18. Fuzzy model-based adaptive synchronization of time-delayed chaotic systems

    International Nuclear Information System (INIS)

    Vasegh, Nastaran; Majd, Vahid Johari

    2009-01-01

    In this paper, fuzzy model-based synchronization of a class of first order chaotic systems described by delayed-differential equations is addressed. To design the fuzzy controller, the chaotic system is modeled by Takagi-Sugeno fuzzy system considering the properties of the nonlinear part of the system. Assuming that the parameters of the chaotic system are unknown, an adaptive law is derived to estimate these unknown parameters, and the stability of error dynamics is guaranteed by Lyapunov theory. Numerical examples are given to demonstrate the validity of the proposed adaptive synchronization approach.

  19. Fuzzy control of small servo motors

    Science.gov (United States)

    Maor, Ron; Jani, Yashvant

    1993-01-01

    To explore the benefits of fuzzy logic and understand the differences between the classical control methods and fuzzy control methods, the Togai InfraLogic applications engineering staff developed and implemented a motor control system for small servo motors. The motor assembly for testing the fuzzy and conventional controllers consist of servo motor RA13M and an encoder with a range of 4096 counts. An interface card was designed and fabricated to interface the motor assembly and encoder to an IBM PC. The fuzzy logic based motor controller was developed using the TILShell and Fuzzy C Development System on an IBM PC. A Proportional-Derivative (PD) type conventional controller was also developed and implemented in the IBM PC to compare the performance with the fuzzy controller. Test cases were defined to include step inputs of 90 and 180 degrees rotation, sine and square wave profiles in 5 to 20 hertz frequency range, as well as ramp inputs. In this paper we describe our approach to develop a fuzzy as well as PH controller, provide details of hardware set-up and test cases, and discuss the performance results. In comparison, the fuzzy logic based controller handles the non-linearities of the motor assembly very well and provides excellent control over a broad range of parameters. Fuzzy technology, as indicated by our results, possesses inherent adaptive features.

  20. Adaptive fuzzy tracking control for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

    Directory of Open Access Journals (Sweden)

    Peng Fei Wang

    2016-10-01

    Full Text Available The design of an adaptive fuzzy tracking control for a flexible air-breathing hypersonic vehicle with actuator constraints is discussed. Based on functional decomposition methodology, velocity and altitude controllers are designed. Fuzzy logic systems are applied to approximate the lumped uncertainty of each subsystem of air-breathing hypersonic vehicle model. Every controllers contain only one adaptive parameter that needs to be updated online with a minimal-learning-parameter scheme. The back-stepping design is not demanded by converting the altitude subsystem into the normal output-feedback formulation, which predigests the design of a controller. The special contribution is that novel auxiliary systems are developed to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the inputs are saturated. Finally, reference trajectory tracking simulation shows the effectiveness of the proposed method in its application to air-breathing hypersonic vehicle control.

  1. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  2. Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain

    Energy Technology Data Exchange (ETDEWEB)

    Luo, Shaohua [School of Automation, Chongqing University, Chongqing 400044, China and College of Mechanical Engineering, Hunan University of Arts and Science, Hunan 415000 (China)

    2014-09-01

    This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

  3. Control of beam halo-chaos using fuzzy logic controller

    International Nuclear Information System (INIS)

    Gao Yuan; Yuan Haiying; Tan Guangxing; Luo Wenguang

    2012-01-01

    Considering the ion beam with initial K-V distribution in the periodic focusing magnetic filed channels (PFCs) as a typical sample, a fuzzy control method for control- ling beam halo-chaos was studied. A fuzzy proportional controller, using output of fuzzy inference as a control factor, was presented for adjusting exterior focusing magnetic field. The stability of controlled system was proved by fuzzy phase plane analysis. The simulation results demonstrate that the chaotic radius of envelope can be controlled to the matched radius via controlling magnetic field. This method was also applied to the multi-particle model. Under the control condition, the beam halos and its regeneration can be eliminated effectively, and that both the compactness and the uniformity of ion beam are improved evidently. Since the exterior magnetic field can be rather easily adjusted by proportional control and the fuzzy logic controller is independent to the mathematical model, this method has adaptive ability and is easily realized in experiment. The research offers a valuable reference for the design of the PFCs in the high- current linear ion accelerators. (authors)

  4. Design and optimization of fuzzy-PID controller for the nuclear reactor power control

    International Nuclear Information System (INIS)

    Liu Cheng; Peng Jinfeng; Zhao Fuyu; Li Chong

    2009-01-01

    This paper introduces a fuzzy proportional-integral-derivative (fuzzy-PID) control strategy, and applies it to the nuclear reactor power control system. At the fuzzy-PID control strategy, the fuzzy logic controller (FLC) is exploited to extend the finite sets of PID gains to the possible combinations of PID gains in stable region and the genetic algorithm to improve the 'extending' precision through quadratic optimization for the membership function (MF) of the FLC. Thus the FLC tunes the gains of PID controller to adapt the model changing with the power. The fuzzy-PID has been designed and simulated to control the reactor power. The simulation results show the favorable performance of the fuzzy-PID controller.

  5. Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

    Science.gov (United States)

    Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

    2011-04-01

    This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

  6. A fuzzy controller with a robust learning function

    International Nuclear Information System (INIS)

    Tanji, Jun-ichi; Kinoshita, Mitsuo

    1987-01-01

    A self-organizing fuzzy controller is able to use linguistic decision rules of control strategy and has a strong adaptive property by virture of its rule learning function. While a simple linguistic description of the learning algorithm first introduced by Procyk, et al. has much flexibility for applications to a wide range of different processes, its detailed formulation, in particular with control stability and learning process convergence, is not clear. In this paper, we describe the formulation of an analytical basis for a self-organizing fuzzy controller by using a method of model reference adaptive control systems (MRACS) for which stability in the adaptive loop is theoretically proven. A detailed formulation is described regarding performance evaluation and rule modification in the rule learning process of the controller. Furthermore, an improved learning algorithm using adaptive rule is proposed. An adaptive rule gives a modification coefficient for a rule change estimating the effect of disturbance occurrence in performance evaluation. The effect of introducing an adaptive rule to improve the learning convergency is described by using a simple iterative formulation. Simulation tests are presented for an application of the proposed self-organizing fuzzy controller to the pressure control system in a Boiling Water Reactor (BWR) plant. Results with the tests confirm the improved learning algorithm has strong convergent properties, even in a very disturbed environment. (author)

  7. Expert system driven fuzzy control application to power reactors

    International Nuclear Information System (INIS)

    Tsoukalas, L.H.; Berkan, R.C.; Upadhyaya, B.R.; Uhrig, R.E.

    1990-01-01

    For the purpose of nonlinear control and uncertainty/imprecision handling, fuzzy controllers have recently reached acclaim and increasing commercial application. The fuzzy control algorithms often require a ''supervisory'' routine that provides necessary heuristics for interface, adaptation, mode selection and other implementation issues. Performance characteristics of an on-line fuzzy controller depend strictly on the ability of such supervisory routines to manipulate the fuzzy control algorithm and enhance its control capabilities. This paper describes an expert system driven fuzzy control design application to nuclear reactor control, for the automated start-up control of the Experimental Breeder Reactor-II. The methodology is verified through computer simulations using a valid nonlinear model. The necessary heuristic decisions are identified that are vitally important for the implemention of fuzzy control in the actual plant. An expert system structure incorporating the necessary supervisory routines is discussed. The discussion also includes the possibility of synthesizing the fuzzy, exact and combined reasoning to include both inexact concepts, uncertainty and fuzziness, within the same environment

  8. Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

    Science.gov (United States)

    Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

    2017-09-01

    This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

  9. Adaptive Fuzzy Integral Sliding-Mode Regulator for Induction Motor Using Nonlinear Sliding Surface

    OpenAIRE

    Yong-Kun Lu

    2015-01-01

    An adaptive fuzzy integral sliding-mode controller using nonlinear sliding surface is designed for the speed regulator of a field-oriented induction motor drive in this paper. Combining the conventional integral sliding surface with fractional-order integral, a nonlinear sliding surface is proposed for the integral sliding-mode speed control, which can overcome the windup problem and the convergence speed problem. An adaptive fuzzy control term is utilized to approximate the uncertainty. The ...

  10. Improved fuzzy PID controller design using predictive functional control structure.

    Science.gov (United States)

    Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

    2017-11-01

    In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Designing a Fuzzy Adaptive Controller for a Rigid joint Two Link Non-Linear Manipulator with Uncertainty

    Directory of Open Access Journals (Sweden)

    Maryam Montazeri

    2013-01-01

    Full Text Available This paper presents a control approach to the fuzzy-adaptive control scheme for rigid manipulators with unknown parameters. Lagrange’s method is employed for computing robot motion dynamics. Stability analysis guaranteed through Lyapunov’s theory using some suitable adaptive rules that make sure all signals in the closed-loop system are bounded and tracking error ones asymptotically reaches to zero. Compared with other controllers, there are some numerical simulations that verify effectiveness of the proposed method. Also, simulation results verify that the proposed controller can deal with uncertainties in the system.

  12. Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Boumediene ALLAOUA

    2009-12-01

    Full Text Available This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.

  13. Design of neuro fuzzy fault tolerant control using an adaptive observer

    International Nuclear Information System (INIS)

    Anita, R.; Umamaheswari, B.; Viswanathan, B.

    2001-01-01

    New methodologies and concepts are developed in the control theory to meet the ever-increasing demands in industrial applications. Fault detection and diagnosis of technical processes have become important in the course of progressive automation in the operation of groups of electric drives. When a group of electric drives is under operation, fault tolerant control becomes complicated. For multiple motors in operation, fault detection and diagnosis might prove to be difficult. Estimation of all states and parameters of all drives is necessary to analyze the actuator and sensor faults. To maintain system reliability, detection and isolation of failures should be performed quickly and accurately, and hardware should be properly integrated. Luenberger full order observer can be used for estimation of the entire states in the system for the detection of actuator and sensor failures. Due to the insensitivity of the Luenberger observer to the system parameter variations, state estimation becomes inaccurate under the varying parameter conditions of the drives. Consequently, the estimation performance deteriorates, resulting in ordinary state observers unsuitable for fault detection technique. Therefore an adaptive observe, which can estimate the system states and parameter and detect the faults simultaneously, is designed in our paper. For a Group of D C drives, there may be parameter variations for some of the drives, and for other drives, there may not be parameter variations depending on load torque, friction, etc. So, estimation of all states and parameters of all drives is carried out using an adaptive observer. If there is any deviation with the estimated values, it is understood that fault has occurred and the nature of the fault, whether sensor fault or actuator fault, is determined by neural fuzzy network, and fault tolerant control is reconfigured. Experimental results with neuro fuzzy system using adaptive observer-based fault tolerant control are good, so as

  14. Study of fuzzy adaptive PID controller on thermal frequency stabilizing laser with double longitudinal modes

    Science.gov (United States)

    Mo, Qingkai; Zhang, Tao; Yan, Yining

    2016-10-01

    There are contradictions among speediness, anti-disturbance performance, and steady-state accuracy caused by traditional PID controller in the existing light source systems of thermal frequency stabilizing laser with double longitudinal modes. In this paper, a new kind of fuzzy adaptive PID controller was designed by combining fuzzy PID control technology and expert system to make frequency stabilizing system obtain the optimal performance. The experiments show that the frequency stability of the designed PID controller is similar to the existing PID controller (the magnitude of frequency stability is less than 10-9 in constant temperature and 10-7 in open air). But the preheating time is shortened obviously (from 10 minutes to 5 minutes) and the anti-disturbance capability is improved significantly (the recovery time needed after strong interference is reduced from 1 minute to 10 seconds).

  15. Flatness-based adaptive fuzzy control of chaotic finance dynamics

    Science.gov (United States)

    Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.

    2017-11-01

    A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop.

  16. Adaptive complementary fuzzy self-recurrent wavelet neural network controller for the electric load simulator system

    Directory of Open Access Journals (Sweden)

    Wang Chao

    2016-03-01

    Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.

  17. DESIGN OF ROBUST COMMAND TO LINE-OF-SIGHT GUIDANCE LAW: A FUZZY ADAPTIVE APPROACH

    Directory of Open Access Journals (Sweden)

    ESMAIL SADEGHINASAB

    2016-11-01

    Full Text Available In this paper, the design of command to line-of-sight (CLOS missile guidance law is addressed. Taking a three dimensional guidance model, the tracking control problem is formulated. To solve the target tracking problem, the feedback linearization controller is first designed. Although such control scheme possesses the simplicity property, but it presents the acceptable performance only in the absence of perturbations. In order to ensure the robustness properties against model uncertainties, a fuzzy adaptive algorithm is proposed with two parts including a fuzzy (Mamdani system, whose rules are constructed based on missile guidance, and a so-called rule modifier to compensate the fuzzy rules, using the negative gradient method. Compared with some previous works, such control strategy provides a faster time response without large control efforts. The performance of feedback linearization controller is also compared with that of fuzzy adaptive strategy via various simulations.

  18. Self tuning fuzzy PID type load and frequency controller

    International Nuclear Information System (INIS)

    Yesil, E.; Guezelkaya, M.; Eksin, I.

    2004-01-01

    In this paper, a self tuning fuzzy PID type controller is proposed for solving the load frequency control (LFC) problem. The fuzzy PID type controller is constructed as a set of control rules, and the control signal is directly deduced from the knowledge base and the fuzzy inference. Moreover, there exists a self tuning mechanism that adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID type fuzzy logic controller in an on-line manner. The self tuning mechanism depends on the peak observer idea, and this idea is modified and adapted to the LFC problem. A two area interconnected system is assumed for demonstrations. The proposed self tuning fuzzy PID type controller has been compared with the fuzzy PID type controller without a self tuning mechanism and the conventional integral controller through some performance indices

  19. Fuzzy power control algorithm for a pressurized water reactor

    International Nuclear Information System (INIS)

    Hah, Y.J.; Lee, B.W.

    1994-01-01

    A fuzzy power control algorithm is presented for automatic reactor power control in a pressurized water reactor (PWR). Automatic power shape control is complicated by the use of control rods with a conventional proportional-integral-differential controller because it is highly coupled with reactivity compensation. Thus, manual shape controls are usually employed even for the limited capability needed for load-following operations including frequency control. In an attempt to achieve automatic power shape control without any design modifications to the core, a fuzzy power control algorithm is proposed. For the fuzzy control, the rule base is formulated based on a multiple-input multiple-output system. The minimum operation rule and the center of area method are implemented for the development of the fuzzy algorithm. The fuzzy power control algorithm has been applied to Yonggwang Nuclear Unit 3. The simulation results show that the fuzzy control can be adapted as a practical control strategy for automatic reactor power control of PWRs during the load-following operations

  20. Implementation of a fuzzy logic/neural network multivariable controller

    International Nuclear Information System (INIS)

    Cordes, G.A.; Clark, D.E.; Johnson, J.A.; Smartt, H.B.; Wickham, K.L.; Larson, T.K.

    1992-01-01

    This paper describes a multivariable controller developed at the Idaho National Engineering Laboratory (INEL) that incorporates both fuzzy logic rules and a neural network. The controller was implemented in a laboratory demonstration and was robust, producing smooth temperature and water level response curves with short time constants. In the future, intelligent control systems will be a necessity for optimal operation of autonomous reactor systems located on earth or in space. Even today, there is a need for control systems that adapt to the changing environment and process. Hybrid intelligent control systems promise to provide this adaptive capability. Fuzzy logic implements our imprecise, qualitative human reasoning. The values of system variables (controller inputs) and control variables (controller outputs) are described in linguistic terms and subdivided into fully overlapping value ranges. The fuzzy rule base describes how combinations of input parameter ranges determine the output control values. Neural networks implement our human learning. In this controller, neural networks were embedded in the software to explore their potential for adding adaptability

  1. A fuzzy logic controller for feedwater regulation in pressurized water reactors

    International Nuclear Information System (INIS)

    Eryuerek, E.E.; Upadhyaya, B.R.; Alguindigue, I.E.

    1994-01-01

    Fuzzy control refers to the application of fuzzy logic theory to control systems. In this paper fuzzy controllers for steam generator water level control and pump speed control are presented, and their performance in the presence of perturbations is discussed. In order to test the robustness of the controllers, their performance is compared with the performance of model based adaptive controllers and traditional PID controllers. The control actions calculated by the fuzzy controllers is have the characteristic of quick and smooth control compared to the others

  2. Using an adaptive fuzzy-logic system to optimize the performances and the reduction of chattering phenomenon in the control of induction motor

    Directory of Open Access Journals (Sweden)

    Barazane Linda

    2009-01-01

    Full Text Available Neural networks and fuzzy inference systems are becoming well recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. Also, by combining these two features, more versatile and robust models, called 'neuro-fuzzy' architectures have been developed. The motivation behind the use of neuro-fuzzy approaches is based on the complexity of real life systems, ambiguities on sensory information or time-varying nature of the system under investigation. In this way, the present contribution concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation and to reduce the chattering phenomenon introduced by sliding mode control, which is very harmful to the actuators in our case and may excite the unmodeled dynamics of the system. The type of the neuro-fuzzy system used here is called:' adaptive neuro fuzzy inference controller (ANFIS'. This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Simulation results reveal some very interesting features. .

  3. Optimized Aircraft Electric Control System Based on Adaptive Tabu Search Algorithm and Fuzzy Logic Control

    Directory of Open Access Journals (Sweden)

    Saifullah Khalid

    2016-09-01

    Full Text Available Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.

  4. Fuzzy Backstepping Sliding Mode Control for Mismatched Uncertain System

    Directory of Open Access Journals (Sweden)

    H. Q. Hou

    2014-06-01

    Full Text Available Sliding mode controllers have succeeded in many control problems that the conventional control theories have difficulties to deal with; however it is practically impossible to achieve high-speed switching control. Therefore, in this paper an adaptive fuzzy backstepping sliding mode control scheme is derived for mismatched uncertain systems. Firstly fuzzy sliding mode controller is designed using backstepping method based on the Lyapunov function approach, which is capable of handling mismatched problem. Then fuzzy sliding mode controller is designed using T-S fuzzy model method, it can improve the performance of the control systems and their robustness. Finally this method of control is applied to nonlinear system as a case study; simulation results are also provided the performance of the proposed controller.

  5. Adaptation of a fuzzy controller’s scaling gains using genetic algorithms for balancing an inverted pendulum

    Directory of Open Access Journals (Sweden)

    Duka Adrian-Vasile

    2011-12-01

    Full Text Available This paper examines the development of a genetic adaptive fuzzy control system for the Inverted Pendulum. The inverted pendulum is a classical problem in Control Engineering, used for testing different control algorithms. The goal is to balance the inverted pendulum in the upright position by controlling the horizontal force applied to its cart. Because it is unstable and has a complicated nonlinear dynamics, the inverted pendulum is a good testbed for the development of nonconventional advanced control techniques. Fuzzy logic technique has been successfully applied to control this type of system, however most of the time the design of the fuzzy controller is done in an ad-hoc manner, and choosing certain parameters (controller gains, membership functions proves difficult. This paper examines the implementation of an adaptive control method based on genetic algorithms (GA, which can be used on-line to produce the adaptation of the fuzzy controller’s gains in order to achieve the stabilization of the pendulum. The performances of the proposed control algorithms are evaluated and shown by means of digital simulation.

  6. Application of robust fuzzy control in power control of nuclear reactor

    International Nuclear Information System (INIS)

    Liu Lei; Luan Xiuchun; Jin Guangyuan; Yu Tao; Rao Su

    2013-01-01

    Robust-fuzzy controller based on T-S fuzzy model was designed for real-time controlling of nuclear reactor power and adapting to the load changing of power grid. Local controller was designed by means of state feedback technique, and the global controller was designed by parallel distributed compensation (PDC) method. The result of solving linear matrix inequalities (LMI) proves that this controller is stable. The simulation shows that the nuclear power can be well controlled in three typical conditions by this controller. (authors)

  7. Adaptive fuzzy-neural-network control for maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

  8. Fuzzy control. Fundamentals, stability and design of fuzzy controllers

    Energy Technology Data Exchange (ETDEWEB)

    Michels, K. [Fichtner GmbH und Co. KG, Stuttgart (Germany); Klawonn, F. [Fachhochschule Braunschweig/Wolfenbuettel (Germany). Fachbereich Informatik; Kruse, R. [Magdeburg Univ. (Germany). Fakultaet Informatik, Abt. Wiss.- und Sprachverarbeitung; Nuernberger, A. (eds.) [California Univ., Berkeley, CA (United States). Computer Science Division

    2006-07-01

    The book provides a critical discussion of fuzzy controllers from the perspective of classical control theory. Special emphases are placed on topics that are of importance for industrial applications, like (self-) tuning of fuzzy controllers, optimisation and stability analysis. The book is written as a textbook for graduate students as well as a comprehensive reference book about fuzzy control for researchers and application engineers. Starting with a detailed introduction to fuzzy systems and control theory the reader is guided to up-to-date research results. (orig.)

  9. Optimal inverse magnetorheological damper modeling using shuffled frog-leaping algorithm–based adaptive neuro-fuzzy inference system approach

    Directory of Open Access Journals (Sweden)

    Xiufang Lin

    2016-08-01

    Full Text Available Magnetorheological dampers have become prominent semi-active control devices for vibration mitigation of structures which are subjected to severe loads. However, the damping force cannot be controlled directly due to the inherent nonlinear characteristics of the magnetorheological dampers. Therefore, for fully exploiting the capabilities of the magnetorheological dampers, one of the challenging aspects is to develop an accurate inverse model which can appropriately predict the input voltage to control the damping force. In this article, a hybrid modeling strategy combining shuffled frog-leaping algorithm and adaptive-network-based fuzzy inference system is proposed to model the inverse dynamic characteristics of the magnetorheological dampers for improving the modeling accuracy. The shuffled frog-leaping algorithm is employed to optimize the premise parameters of the adaptive-network-based fuzzy inference system while the consequent parameters are tuned by a least square estimation method, here known as shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach. To evaluate the effectiveness of the proposed approach, the inverse modeling results based on the shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach are compared with those based on the adaptive-network-based fuzzy inference system and genetic algorithm–based adaptive-network-based fuzzy inference system approaches. Analysis of variance test is carried out to statistically compare the performance of the proposed methods and the results demonstrate that the shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system strategy outperforms the other two methods in terms of modeling (training accuracy and checking accuracy.

  10. Why fuzzy controllers should be fuzzy

    International Nuclear Information System (INIS)

    Nowe, A.

    1996-01-01

    Fuzzy controllers are usually looked at as crisp valued mappings especially when artificial intelligence learning techniques are used to build up the controller. By doing so the semantics of a fuzzy conclusion being a fuzzy restriction on the viable control actions is non-existing. In this paper the authors criticise from an approximation point of view using a fuzzy controller to express a crisp mapping does not seem the right way to go. Secondly it is illustrated that interesting information is contained in a fuzzy conclusion when indeed this conclusion is considered as a fuzzy restriction. This information turns out to be very valuable when viability problems are concerned, i.e. problems where the objective is to keep a system within predefined boundaries

  11. Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

    Science.gov (United States)

    Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

    2016-05-09

    In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

  12. Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

    Science.gov (United States)

    Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.

    2018-02-01

    This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

  13. Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

    Science.gov (United States)

    Mitra, Sunanda; Pemmaraju, Surya

    1992-01-01

    Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

  14. Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Syed Zulqadar Hassan

    2017-03-01

    Full Text Available An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

  15. Introduction to n-adaptive fuzzy models to analyze public opinion on AIDS

    CERN Document Server

    Kandasamy, D W B V; Kandasamy, Dr.W.B.Vasantha; Smarandache, Dr.Florentin

    2006-01-01

    There are many fuzzy models like Fuzzy matrices, Fuzzy Cognitive Maps, Fuzzy relational Maps, Fuzzy Associative Memories, Bidirectional Associative memories and so on. But almost all these models can give only one sided solution like hidden pattern or a resultant output vector dependent on the input vector depending in the problem at hand. So for the first time we have defined a n-adaptive fuzzy model which can view or analyze the problem in n ways (n >=2) Though we have defined these n- adaptive fuzzy models theorectically we are not in a position to get a n-adaptive fuzzy model for n > 2 for practical real world problems. The highlight of this model is its capacity to analyze the same problem in different ways thereby arriving at various solutions that mirror multiple perspectives. We have used the 2-adaptive fuzzy model having the two fuzzy models, fuzzy matrices model and BAMs viz. model to analyze the views of public about HIV/ AIDS disease, patient and the awareness program. This book has five chapters ...

  16. Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

    Science.gov (United States)

    Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

    2018-05-10

    In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Fuzzy stochastic damage mechanics (FSDM based on fuzzy auto-adaptive control theory

    Directory of Open Access Journals (Sweden)

    Ya-jun Wang

    2012-06-01

    Full Text Available In order to fully interpret and describe damage mechanics, the origin and development of fuzzy stochastic damage mechanics were introduced based on the analysis of the harmony of damage, probability, and fuzzy membership in the interval of [0,1]. In a complete normed linear space, it was proven that a generalized damage field can be simulated through β probability distribution. Three kinds of fuzzy behaviors of damage variables were formulated and explained through analysis of the generalized uncertainty of damage variables and the establishment of a fuzzy functional expression. Corresponding fuzzy mapping distributions, namely, the half-depressed distribution, swing distribution, and combined swing distribution, which can simulate varying fuzzy evolution in diverse stochastic damage situations, were set up. Furthermore, through demonstration of the generalized probabilistic characteristics of damage variables, the cumulative distribution function and probability density function of fuzzy stochastic damage variables, which show β probability distribution, were modified according to the expansion principle. The three-dimensional fuzzy stochastic damage mechanical behaviors of the Longtan rolled-concrete dam were examined with the self-developed fuzzy stochastic damage finite element program. The statistical correlation and non-normality of random field parameters were considered comprehensively in the fuzzy stochastic damage model described in this paper. The results show that an initial damage field based on the comprehensive statistical evaluation helps to avoid many difficulties in the establishment of experiments and numerical algorithms for damage mechanics analysis.

  18. Direct adaptive fuzzy control of a translating piezoelectric flexible manipulator driven by a pneumatic rodless cylinder

    Science.gov (United States)

    Qiu, Zhi-cheng; Wang, Bin; Zhang, Xian-min; Han, Jian-da

    2013-04-01

    This study presents a novel translating piezoelectric flexible manipulator driven by a rodless cylinder. Simultaneous positioning control and vibration suppression of the flexible manipulator is accomplished by using a hybrid driving scheme composed of the pneumatic cylinder and a piezoelectric actuator. Pulse code modulation (PCM) method is utilized for the cylinder. First, the system dynamics model is derived, and its standard multiple input multiple output (MIMO) state-space representation is provided. Second, a composite proportional derivative (PD) control algorithms and a direct adaptive fuzzy control method are designed for the MIMO system. Also, a time delay compensation algorithm, bandstop and low-pass filters are utilized, under consideration of the control hysteresis and the caused high-frequency modal vibration due to the long stroke of the cylinder, gas compression and nonlinear factors of the pneumatic system. The convergence of the closed loop system is analyzed. Finally, experimental apparatus is constructed and experiments are conducted. The effectiveness of the designed controllers and the hybrid driving scheme is verified through simulation and experimental comparison studies. The numerical simulation and experimental results demonstrate that the proposed system scheme of employing the pneumatic drive and piezoelectric actuator can suppress the vibration and achieve the desired positioning location simultaneously. Furthermore, the adopted adaptive fuzzy control algorithms can significantly enhance the control performance.

  19. Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems

    Energy Technology Data Exchange (ETDEWEB)

    Shieh, M-Y; Chang, K-H [Department of E. E., Southern Taiwan University, 1 Nantai St., YungKang City, Tainan County 71005, Taiwan (China); Lia, Y-S [Executive Director Office, ITRI, Southern Taiwan Innovation Park, Tainan County, Taiwan (China)], E-mail: myshieh@mail.stut.edu.tw

    2008-02-15

    This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface.

  20. Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems

    International Nuclear Information System (INIS)

    Shieh, M-Y; Chang, K-H; Lia, Y-S

    2008-01-01

    This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface

  1. A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI

    Directory of Open Access Journals (Sweden)

    Fengqi Zhang

    2016-11-01

    Full Text Available This paper presents a new energy management system based on equivalent consumption minimization strategy (ECMS for hybrid electric vehicles. The aim is to enhance fuel economy and impose state of charge (SoC charge-sustainability. First, the relationship between the equivalent factor (EF of ECMS and the co-state of pontryagin’s minimum principle (PMP is derived. Second, a new method of implementing the adaptation law using fuzzy proportional plus integral (PI controller is developed to adjust EF for ECMS in real-time. This adaptation law is more robust than one with constant EF due to the variation of EF as well as driving cycle. Finally, simulations for two driving cycles using ECMS are conducted as opposed to the commonly used rule-based (RB control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results confirm that ECMS with Fuzzy PI adaptation law is more robust than ECMS with constant EF as well as PI adaptation law and it achieves significant improvements compared with RB in terms of fuel economy, which is enhanced by 4.44% and 14.7% for china city bus cycle and economic commission of Europe (ECE cycle, respectively.

  2. Fuzzeval: A Fuzzy Controller-Based Approach in Adaptive Learning for Backgammon Game

    DEFF Research Database (Denmark)

    Heinze, Mikael; Ortiz-Arroyo, Daniel; Larsen, Henrik Legind

    2005-01-01

    In this paper we investigate the effectiveness of applying fuzzy controllers to create strong computer player programs in the domain of backgammon. Fuzzeval, our proposed mechanism, consists of a fuzzy controller that dynamically evaluates the perceived strength of the board configurations it re-...

  3. Lag synchronization of unknown chaotic delayed Yang-Yang-type fuzzy neural networks with noise perturbation based on adaptive control and parameter identification.

    Science.gov (United States)

    Xia, Yonghui; Yang, Zijiang; Han, Maoan

    2009-07-01

    This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.

  4. A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes.

    Science.gov (United States)

    Savran, Aydogan; Kahraman, Gokalp

    2014-03-01

    We develop a novel adaptive tuning method for classical proportional-integral-derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input-output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities. © 2013 Published by ISA on behalf of ISA.

  5. Fuzzy algorithm for an automatic reactor power control in a PWR

    International Nuclear Information System (INIS)

    Hah, Yung Joon; Song, In Ho; Yu, Sung Sik; Choi, Jung In; Lee, Byong Whi

    1994-01-01

    A fuzzy algorithm is presented for automatic reactor power control in a pressurized water reactor. Automatic power shape control is complicated by the use of control rods because it is highly coupled with reactivity compensation. Thus, manual shape controls are usually employed even for the limited capability for the load - follow operation including frequency control. In an attempt to achieve automatic power shape control without any design modification of the core, a fuzzy power control algorithm is proposed. For the fuzzy control, the rule base is formulated based on a multi - input multi - output system. The minimum operation rule and the center of area method are implemented for the development of the fuzzy algorithm. The fuzzy power control algorithm has been applied to the Yonggwang Nuclear Unit 3. The simulation results show that the fuzzy control can be adapted as a practical control strategy for automatic reactor power control of the pressurized water reactor during the load - follow operation

  6. Control of a mechanical gripper with a fuzzy controller; Control de una garra robotizada mediante un controlador borroso

    Energy Technology Data Exchange (ETDEWEB)

    Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.

    1995-07-01

    A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers.

  7. Control of a mechanical gripper with a fuzzy controller; Control de una garra robotizada mediante un controlador borroso

    Energy Technology Data Exchange (ETDEWEB)

    Alberdi, J.; Barcala, J.M.; Gamero, E.; Navarrete, J.J.

    1995-07-01

    A fuzzy logic system is used to control a mechanical gripper. System is based in a NLX230 fuzzy micro controller. Control rules are programmed by a 68020 microprocessor in the micro controller memory. Stress and its derived are used as feedback signals in the control. This system can adapt its effort to the mechanical resistance of the object between the fingers. (Author)

  8. Design of adaptive switching control for hypersonic aircraft

    Directory of Open Access Journals (Sweden)

    Xin Jiao

    2015-10-01

    Full Text Available This article proposes a novel adaptive switching control of hypersonic aircraft based on type-2 Takagi–Sugeno–Kang fuzzy sliding mode control and focuses on the problem of stability and smoothness in the switching process. This method uses full-state feedback to linearize the nonlinear model of hypersonic aircraft. Combining the interval type-2 Takagi–Sugeno–Kang fuzzy approach with sliding mode control keeps the adaptive switching process stable and smooth. For rapid stabilization of the system, the adaptive laws use a direct constructive Lyapunov analysis together with an established type-2 Takagi–Sugeno–Kang fuzzy logic system. Simulation results indicate that the proposed control scheme can maintain the stability and smoothness of switching process for the hypersonic aircraft.

  9. Fuzzy regulator design for wind turbine yaw control.

    Science.gov (United States)

    Theodoropoulos, Stefanos; Kandris, Dionisis; Samarakou, Maria; Koulouras, Grigorios

    2014-01-01

    This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness.

  10. Adaptive fuzzy control of neutron power of the TRIGA Mark III reactor

    International Nuclear Information System (INIS)

    Rojas R, E.

    2014-01-01

    The design and implementation of an identification and control scheme of the TRIGA Mark III research nuclear reactor of the Instituto Nacional de Investigaciones Nucleares (ININ) of Mexico is presented in this thesis work. The identification of the reactor dynamics is carried out using fuzzy logic based systems, in which a learning process permits the adjustment of the membership function parameters by means of techniques based on neural networks and bio-inspired algorithms. The resulting identification system is a useful tool that allows the emulation of the reactor power behavior when different types of insertions of reactivity are applied into the core. The identification of the power can also be used for the tuning of the parameters of a control system. On the other hand, the regulation of the reactor power is carried out by means of an adaptive and stable fuzzy control scheme. The control law is derived using the input-output linearization technique, which permits the introduction of a desired power profile for the plant to follow asymptotically. This characteristic is suitable for managing the ascent of power from an initial level n o up to a predetermined final level n f . During the increase of power, a constraint related to the rate of change in power is considered by the control scheme, thus minimizing the occurrence of a safety reactor shutdown due to a low reactor period value. Furthermore, the theory of stability in the sense of Lyapunov is used to obtain a supervisory control law which maintains the power error within a tolerance region, thus guaranteeing the stability of the power of the closed loop system. (Author)

  11. Velocity control of a secondary controlled closed-loop hydrostatic transmission system using an adaptive fuzzy sliding mode controller

    Energy Technology Data Exchange (ETDEWEB)

    Do, Hoang Thinh; Ahn, Kyoung Kwan [University of Ulsan, Ulsan (Korea, Republic of)

    2013-03-15

    A secondary-controlled hydrostatic transmission system (SC-HST), which considered being an energy-saving system, can recuperate most of the lost vehicle kinetic energy in decelerating and braking time and it shows advantage in fuel economy improvement of vehicle. Almost secondary control units (SCU) in SC-HST inherently contain nonlinear characteristics such as dead-zone input. Therefore, it is difficult to obtain precise position or velocity control by conventional linear controllers. This problem limits the application of SC-HST in industry and mobile vehicle. This paper gives a description of SC-HST and proposes an adaptive fuzzy sliding mode controller (AFSMC) for velocity control of SCU. Experiments were carried out in the condition of disturbance load by using both the proposed controller and PID controller for the comparison and evaluation of the effectiveness of the proposed controller. The experimental results showed that the proposed controller was excellent from the standpoints of performance and stability for the velocity control of SC-HST.

  12. Fuzzy Logic Control of Adaptive ARQ for Video Distribution over a Bluetooth Wireless Link

    Directory of Open Access Journals (Sweden)

    R. Razavi

    2007-01-01

    Full Text Available Bluetooth's default automatic repeat request (ARQ scheme is not suited to video distribution resulting in missed display and decoded deadlines. Adaptive ARQ with active discard of expired packets from the send buffer is an alternative approach. However, even with the addition of cross-layer adaptation to picture-type packet importance, ARQ is not ideal in conditions of a deteriorating RF channel. The paper presents fuzzy logic control of ARQ, based on send buffer fullness and the head-of-line packet's deadline. The advantage of the fuzzy logic approach, which also scales its output according to picture type importance, is that the impact of delay can be directly introduced to the model, causing retransmissions to be reduced compared to all other schemes. The scheme considers both the delay constraints of the video stream and at the same time avoids send buffer overflow. Tests explore a variety of Bluetooth send buffer sizes and channel conditions. For adverse channel conditions and buffer size, the tests show an improvement of at least 4 dB in video quality compared to nonfuzzy schemes. The scheme can be applied to any codec with I-, P-, and (possibly B-slices by inspection of packet headers without the need for encoder intervention.

  13. Hierarchical type-2 fuzzy aggregation of fuzzy controllers

    CERN Document Server

    Cervantes, Leticia

    2016-01-01

    This book focuses on the fields of fuzzy logic, granular computing and also considering the control area. These areas can work together to solve various control problems, the idea is that this combination of areas would enable even more complex problem solving and better results. In this book we test the proposed method using two benchmark problems: the total flight control and the problem of water level control for a 3 tank system. When fuzzy logic is used it make it easy to performed the simulations, these fuzzy systems help to model the behavior of a real systems, using the fuzzy systems fuzzy rules are generated and with this can generate the behavior of any variable depending on the inputs and linguistic value. For this reason this work considers the proposed architecture using fuzzy systems and with this improve the behavior of the complex control problems.

  14. Fuzzy Control Tutorial

    DEFF Research Database (Denmark)

    Dotoli, M.; Jantzen, Jan

    1999-01-01

    The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....

  15. Precision position control of servo systems using adaptive back-stepping and recurrent fuzzy neural networks

    International Nuclear Information System (INIS)

    Kim, Han Me; Kim, Jong Shik; Han, Seong Ik

    2009-01-01

    To improve position tracking performance of servo systems, a position tracking control using adaptive back-stepping control(ABSC) scheme and recurrent fuzzy neural networks(RFNN) is proposed. An adaptive rule of the ABSC based on system dynamics and dynamic friction model is also suggested to compensate nonlinear dynamic friction characteristics. However, it is difficult to reduce the position tracking error of servo systems by using only the ABSC scheme because of the system uncertainties which cannot be exactly identified during the modeling of servo systems. Therefore, in order to overcome system uncertainties and then to improve position tracking performance of servo systems, the RFNN technique is additionally applied to the servo system. The feasibility of the proposed control scheme for a servo system is validated through experiments. Experimental results show that the servo system with ABS controller based on the dual friction observer and RFNN including the reconstruction error estimator can achieve desired tracking performance and robustness

  16. Fuzzy Regulator Design for Wind Turbine Yaw Control

    Directory of Open Access Journals (Sweden)

    Stefanos Theodoropoulos

    2014-01-01

    Full Text Available This paper proposes the development of an advanced fuzzy logic controller which aims to perform intelligent automatic control of the yaw movement of wind turbines. The specific fuzzy controller takes into account both the wind velocity and the acceptable yaw error correlation in order to achieve maximum performance efficacy. In this way, the proposed yaw control system is remarkably adaptive to the existing conditions. In this way, the wind turbine is enabled to retain its power output close to its nominal value and at the same time preserve its yaw system from pointless movement. Thorough simulation tests evaluate the proposed system effectiveness.

  17. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

    Directory of Open Access Journals (Sweden)

    Wei Jin

    2016-12-01

    Full Text Available Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC, atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

  18. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

    Science.gov (United States)

    Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

    2016-01-01

    Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

  19. An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

    Science.gov (United States)

    Ajay Kumar, M.; Srikanth, N. V.

    2014-03-01

    In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

  20. Developing a multipurpose sun tracking system using fuzzy control

    Energy Technology Data Exchange (ETDEWEB)

    Alata, Mohanad [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan)]. E-mail: alata@just.edu.jo; Al-Nimr, M.A. [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan); Qaroush, Yousef [Department of Mechanical Engineering, Jordan University of Science and Technology (JUST), PO Box 3030, Irbid 22110 (Jordan)

    2005-05-01

    The present work demonstrates the design and simulation of time controlled step sun tracking systems that include: one axis sun tracking with the tilted aperture equal to the latitude angle, equatorial two axis sun tracking and azimuth/elevation sun tracking. The first order Sugeno fuzzy inference system is utilized for modeling and controller design. In addition, an estimation of the insolation incident on a two axis sun tracking system is determined by fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm, along with least square estimation (LSE), generates the fuzzy rules that describe the relationship between the input/output data of solar angles that change with time. The fuzzy rules are tuned by an adaptive neuro-fuzzy inference system (ANFIS). Finally, an open loop control system is designed for each of the previous types of sun tracking systems. The results are shown using simulation and virtual reality. The site of application is chosen at Amman, Jordan (32 deg. North, 36 deg. East), and the period of controlling and simulating each type of tracking system is the year 2003.

  1. Modal-space reference-model-tracking fuzzy control of earthquake excited structures

    Science.gov (United States)

    Park, Kwan-Soon; Ok, Seung-Yong

    2015-01-01

    This paper describes an adaptive modal-space reference-model-tracking fuzzy control technique for the vibration control of earthquake-excited structures. In the proposed approach, the fuzzy logic is introduced to update optimal control force so that the controlled structural response can track the desired response of a reference model. For easy and practical implementation, the reference model is constructed by assigning the target damping ratios to the first few dominant modes in modal space. The numerical simulation results demonstrate that the proposed approach successfully achieves not only the adaptive fault-tolerant control system against partial actuator failures but also the robust performance against the variations of the uncertain system properties by redistributing the feedback control forces to the available actuators.

  2. A two-stage planning and control model toward Economically Adapted Power Distribution Systems using analytical hierarchy processes and fuzzy optimization

    Energy Technology Data Exchange (ETDEWEB)

    Schweickardt, Gustavo [Instituto de Economia Energetica, Fundacion Bariloche, Centro Atomico Bariloche - Pabellon 7, Av. Bustillo km 9500, 8400 Bariloche (Argentina); Miranda, Vladimiro [INESC Porto, Instituto de Engenharia de Sistemas e Computadores do Porto and FEUP, Faculdade de Engenharia da Universidade do Porto, R. Dr. Roberto Frias, 378, 4200-465 Porto (Portugal)

    2009-07-15

    This work presents a model to evaluate the Distribution System Dynamic De-adaptation respecting its planning for a given period of Tariff Control. The starting point for modeling is brought about by the results from a multi-criteria method based on Fuzzy Dynamic Programming and on Analytic Hierarchy Processes applied in a mid/short-term horizon (stage 1). Then, the decision-making activities using the Hierarchy Analytical Processes will allow defining, for a Control of System De-adaptation (stage 2), a Vector to evaluate the System Dynamic Adaptation. It is directly associated to an eventual series of inbalances that take place during its evolution. (author)

  3. Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

    Directory of Open Access Journals (Sweden)

    Ding Fang

    2013-06-01

    Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

  4. Fuzzy Control Teaching Models

    Directory of Open Access Journals (Sweden)

    Klaus-Dietrich Kramer

    2016-05-01

    Full Text Available Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tune the system behavior. In order to support the development process a graphical user interface is needed to display the fuzzy terms under real time conditions, especially with a debug system and trace functionality. The experiences with such a programming tool, the Fuzzy Control Design Tool (FHFCE Tool, and four fuzzy teaching models will be presented in this paper. The methodical and didactical objective in the utilization of these teaching models is to develop solution strategies using Computational Intelligence (CI applications for Fuzzy Controllers in order to analyze different algorithms of inference or defuzzyfication and to verify and tune those systems efficiently.

  5. Supervisory Adaptive Network-Based Fuzzy Inference System (SANFIS Design for Empirical Test of Mobile Robot

    Directory of Open Access Journals (Sweden)

    Yi-Jen Mon

    2012-10-01

    Full Text Available A supervisory Adaptive Network-based Fuzzy Inference System (SANFIS is proposed for the empirical control of a mobile robot. This controller includes an ANFIS controller and a supervisory controller. The ANFIS controller is off-line tuned by an adaptive fuzzy inference system, the supervisory controller is designed to compensate for the approximation error between the ANFIS controller and the ideal controller, and drive the trajectory of the system onto a specified surface (called the sliding surface or switching surface while maintaining the trajectory onto this switching surface continuously to guarantee the system stability. This SANFIS controller can achieve favourable empirical control performance of the mobile robot in the empirical tests of driving the mobile robot with a square path. Practical experimental results demonstrate that the proposed SANFIS can achieve better control performance than that achieved using an ANFIS controller for empirical control of the mobile robot.

  6. Safety critical application of fuzzy control

    International Nuclear Information System (INIS)

    Schildt, G.H.

    1995-01-01

    After an introduction into safety terms a short description of fuzzy logic will be given. Especially, for safety critical applications of fuzzy controllers a possible controller structure will be described. The following items will be discussed: Configuration of fuzzy controllers, design aspects like fuzzfiication, inference strategies, defuzzification and types of membership functions. As an example a typical fuzzy rule set will be presented. Especially, real-time behaviour a fuzzy controllers is mentioned. An example of fuzzy controlling for temperature control purpose within a nuclear reactor together with membership functions and inference strategy of such a fuzzy controller will be presented. (author). 4 refs, 17 figs

  7. Systematic methods for the design of a class of fuzzy logic controllers

    Science.gov (United States)

    Yasin, Saad Yaser

    2002-09-01

    Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental

  8. Adaptive Neuro-Fuzzy Based Gain Controller for Erbium-Doped Fiber Amplifiers

    Directory of Open Access Journals (Sweden)

    YUCEL, M.

    2017-02-01

    Full Text Available Erbium-doped fiber amplifiers (EDFA must have a flat gain profile which is a very important parameter such as wavelength division multiplexing (WDM and dense WDM (DWDM applications for long-haul optical communication systems and networks. For this reason, it is crucial to hold a stable signal power per optical channel. For the purpose of overcoming performance decline of optical networks and long-haul optical systems, the gain of the EDFA must be controlled for it to be fixed at a high speed. In this study, due to the signal power attenuation in long-haul fiber optic communication systems and non-equal signal amplification in each channel, an automatic gain controller (AGC is designed based on the adaptive neuro-fuzzy inference system (ANFIS for EDFAs. The intelligent gain controller is implemented and the performance of this new electronic control method is demonstrated. The proposed ANFIS-based AGC-EDFA uses the experimental dataset to produce the ANFIS-based sets and the rule base. Laser diode currents are predicted within the accuracy rating over 98 percent with the proposed ANFIS-based system. Upon comparing ANFIS-based AGC-EDFA and experimental results, they were found to be very close and compatible.

  9. Intelligent control of non-linear dynamical system based on the adaptive neurocontroller

    Science.gov (United States)

    Engel, E.; Kovalev, I. V.; Kobezhicov, V.

    2015-10-01

    This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the base of system's state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simulation results show that the proposed controller scheme achieves real-time control speed and the competitive performance, as compared to PID, fuzzy logic controllers.

  10. Simulation of the fuzzy-smith control system for the high temperature gas cooled reactor

    International Nuclear Information System (INIS)

    Li Deheng; Xu Xiaolin; Zheng Jie; Guo Renjun; Zhang Guifen

    1997-01-01

    The Fuzzy-Smith pre-estimate controller to solve the control of the big delay system is developed, accompanied with the development of the mathematical model of the 10 MW high temperature gas cooled test reactor (HTR-10) and the design of its control system. The simulation results show the Fuzzy-Smith pre-estimate controller has the advantages of both fuzzy control and Smith pre-estimate controller; it has better compensation to the delay and better adaptability to the parameter change of the control object. So it is applicable to the design of the control system for the high temperature gas cooled reactor

  11. Fuzzy Behaviors for Control of Mobile Robots

    Directory of Open Access Journals (Sweden)

    Saleh Zein-Sabatto

    2003-02-01

    Full Text Available In this research work, an RWI B-14 robot has been used as the development platform to embody some basic behaviors that can be combined to build more complex robotics behaviors. Emergency, avoid-obstacle, left wall- following, right wall-following, and move-to-point behaviors have been designed and embodied as basic robot behaviors. The basic behaviors developed in this research are designed based on fuzzy control technique and are integrated and coordinated to from complex robotics system. More behaviors can be added into the system as needed. A robot task can be defined by the user and executed by the intelligent robot control system. Testing results showed that fuzzy behaviors made the robot move intelligently and adapt to changes in its environment.

  12. Fuzzy Control of Cold Storage Refrigeration System with Dynamic Coupling Compensation

    Directory of Open Access Journals (Sweden)

    Xiliang Ma

    2018-01-01

    Full Text Available Cold storage refrigeration systems possess the characteristics of multiple input and output and strong coupling, which brings challenges to the optimize control. To reduce the adverse effects of the coupling and improve the overall control performance of cold storage refrigeration systems, a control strategy with dynamic coupling compensation was studied. First, dynamic model of a cold storage refrigeration system was established based on the requirements of the control system. At the same time, the coupling between the components was studied. Second, to reduce the adverse effects of the coupling, a fuzzy controller with dynamic coupling compensation was designed. As for the fuzzy controller, a self-tuning fuzzy controller was served as the primary controller, and an adaptive neural network was adopted to compensate the dynamic coupling. Finally, the proposed control strategy was employed to the cold storage refrigeration system, and simulations were carried out in the condition of start-up, variable load, and variable degree of superheat, respectively. The simulation results verify the effectiveness of the fuzzy control method with dynamic coupling compensation.

  13. Fuzzy robust nonlinear control approach for electro-hydraulic flight motion simulator

    Directory of Open Access Journals (Sweden)

    Han Songshan

    2015-02-01

    Full Text Available A fuzzy robust nonlinear controller for hydraulic rotary actuators in flight motion simulators is proposed. Compared with other three-order models of hydraulic rotary actuators, the proposed controller based on first-order nonlinear model is more easily applied in practice, whose control law is relatively simple. It not only does not need high-order derivative of desired command, but also does not require the feedback signals of velocity, acceleration and jerk of hydraulic rotary actuators. Another advantage is that it does not rely on any information of friction, inertia force and external disturbing force/torque, which are always difficult to resolve in flight motion simulators. Due to the special composite vane seals of rectangular cross-section and goalpost shape used in hydraulic rotary actuators, the leakage model is more complicated than that of traditional linear hydraulic cylinders. Adaptive multi-input single-output (MISO fuzzy compensators are introduced to estimate nonlinear uncertain functions about leakage and bulk modulus. Meanwhile, the decomposition of the uncertainties is used to reduce the total number of fuzzy rules. Different from other adaptive fuzzy compensators, a discontinuous projection mapping is employed to guarantee the estimation process to be bounded. Furthermore, with a sufficient number of fuzzy rules, the controller theoretically can guarantee asymptotic tracking performance in the presence of the above uncertainties, which is very important for high-accuracy tracking control of flight motion simulators. Comparative experimental results demonstrate the effectiveness of the proposed algorithm, which can guarantee transient performance and better final accurate tracking in the presence of uncertain nonlinearities and parametric uncertainties.

  14. Adaptive fuzzy bilinear observer based synchronization design for generalized Lorenz system

    International Nuclear Information System (INIS)

    Baek, Jaeho; Lee, Heejin; Kim, Seungwoo; Park, Mignon

    2009-01-01

    This Letter proposes an adaptive fuzzy bilinear observer (FBO) based synchronization design for generalized Lorenz system (GLS). The GLS can be described to TS fuzzy bilinear generalized Lorenz model (FBGLM) with their states immeasurable and their parameters unknown. We design an adaptive FBO based on TS FBGLM for synchronization. Lyapunov theory is employed to guarantee the stability of error dynamic system via linear matrix equalities (LMIs) and to derive the adaptive laws to estimate unknown parameters. Numerical example is given to demonstrate the validity of our proposed adaptive FBO approach for synchronization.

  15. Constant Cutting Force Control for CNC Machining Using Dynamic Characteristic-Based Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Hengli Liu

    2015-01-01

    Full Text Available This paper presents a dynamic characteristic-based fuzzy adaptive control algorithm (DCbFACA to avoid the influence of cutting force changing rapidly on the machining stability and precision. The cutting force is indirectly obtained in real time by monitoring and extraction of the motorized spindle current, the feed speed is fuzzy adjusted online, and the current was used as a feedback to control cutting force and maintain the machining process stable. Different from the traditional fuzzy control methods using the experience-based control rules, and according to the complex nonlinear characteristics of CNC machining, the power bond graph method is implemented to describe the dynamic characteristics of process, and then the appropriate variation relations are achieved between current and feed speed, and the control rules are optimized and established based on it. The numerical results indicated that DCbFACA can make the CNC machining process more stable and improve the machining precision.

  16. Supervisory System and Multivariable Control Applying Weighted Fuzzy-PID Logic in an Alcoholic Fermentation Process

    Directory of Open Access Journals (Sweden)

    Márcio Mendonça

    2015-10-01

    Full Text Available In this work, it is analyzed a multivariate system control of an alcoholic fermentation process with no minimum phase. The control is made with PID classic controllers associated with a supervisory system based on Fuzzy Systems. The Fuzzy system, a priori, send set-points to PID controllers, but also adds protection functions, such as if the biomass valued is at zero or very close. The Fuzzy controller changes the campaign to prevent or mitigate the paralyzation of the process. Three control architectures based on Fuzzy Control Systems are presented and compared in performance with classic control in different campaigns. The third architecture, in particular, adds an adaptive function. A brief summary of Fuzzy theory and correlated works will be presented. And, finally simulations results, conclusions and future works end the article.

  17. Adaptive time-variant models for fuzzy-time-series forecasting.

    Science.gov (United States)

    Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

    2010-12-01

    A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

  18. Integrated Power and Attitude Control Design of Satellites Based on a Fuzzy Adaptive Disturbance Observer Using Variable-Speed Control Moment Gyros

    Directory of Open Access Journals (Sweden)

    Zhongyi Chu

    2016-01-01

    Full Text Available To satisfy the requirements for small satellites that seek agile slewing with peak power, this paper investigates integrated power and attitude control using variable-speed control moment gyros (VSCMGs that consider the mass and inertia of gimbals and wheels. The paper also details the process for developing the controller by considering various environments in which the controller may be implemented. A fuzzy adaptive disturbance observer (FADO is proposed to estimate and compensate for the effects of equivalent disturbances. The algorithms can simultaneously track attitude and power. The simulation results illustrate the effectiveness of the control approach, which exhibits an improvement of 80 percent compared with alternate approaches that do not employ a FADO.

  19. Novel Observer Scheme of Fuzzy-MRAS Sensorless Speed Control of Induction Motor Drive

    Science.gov (United States)

    Chekroun, S.; Zerikat, M.; Mechernene, A.; Benharir, N.

    2017-01-01

    This paper presents a novel approach Fuzzy-MRAS conception for robust accurate tracking of induction motor drive operating in a high-performance drives environment. Of the different methods for sensorless control of induction motor drive the model reference adaptive system (MRAS) finds lot of attention due to its good performance. The analysis of the sensorless vector control system using MRAS is presented and the resistance parameters variations and speed observer using new Fuzzy Self-Tuning adaptive IP Controller is proposed. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The present approach helps to achieve a good dynamic response, disturbance rejection and low to plant parameter variations of the induction motor. In order to verify the performances of the proposed observer and control algorithms and to test behaviour of the controlled system, numerical simulation is achieved. Simulation results are presented and discussed to shown the validity and the performance of the proposed observer.

  20. The foundations of fuzzy control

    CERN Document Server

    Lewis, Harold W

    1997-01-01

    Harold Lewis applied a cross-disciplinary approach in his highly accessible discussion of fuzzy control concepts. With the aid of fifty-seven illustrations, he thoroughly presents a unique mathematical formalism to explain the workings of the fuzzy inference engine and a novel test plant used in the research. Additionally, the text posits a new viewpoint on why fuzzy control is more popular in some countries than in others. A direct and original view of Japanese thinking on fuzzy control methods, based on the author's personal knowledge of - and association with - Japanese fuzzy research, is also included.

  1. A computationally efficient fuzzy control s

    Directory of Open Access Journals (Sweden)

    Abdel Badie Sharkawy

    2013-12-01

    Full Text Available This paper develops a decentralized fuzzy control scheme for MIMO nonlinear second order systems with application to robot manipulators via a combination of genetic algorithms (GAs and fuzzy systems. The controller for each degree of freedom (DOF consists of a feedforward fuzzy torque computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line using GAs, whereas not only the parameters but also the structure of the fuzzy system is optimized. The feedback fuzzy PD system, on the other hand, is used to keep the closed-loop stable. The rule base consists of only four rules per each DOF. Furthermore, the fuzzy feedback system is decentralized and simplified leading to a computationally efficient control scheme. The proposed control scheme has the following advantages: (1 it needs no exact dynamics of the system and the computation is time-saving because of the simple structure of the fuzzy systems and (2 the controller is robust against various parameters and payload uncertainties. The computational complexity of the proposed control scheme has been analyzed and compared with previous works. Computer simulations show that this controller is effective in achieving the control goals.

  2. Achieving transparency and adaptivity in fuzzy control framework : an application to power transformers predictive overload system

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.; Ippolito, L.; Siano, P.

    2004-01-01

    From a technologic point of view, the problem of fuzzy control deals with the real implementation of a controller on a specific hardware. Today, the market of micro-controller offers different solutions able to implement a fuzzy controller varying from application domains to programming language

  3. Speed control of induction motor using fuzzy recursive least squares technique

    OpenAIRE

    Santiago Sánchez; Eduardo Giraldo

    2008-01-01

    A simple adaptive controller design is presented in this paper, the control system uses the adaptive fuzzy logic, sliding modes and is trained with the recursive least squares technique. The problem of parameter variation is solved with the adaptive controller; the use of an internal PI regulator produces that the speed control of the induction motor be achieved by the stator currents instead the input voltage. The rotor-flux oriented coordinated system model is used to develop and test the c...

  4. Speed control of induction motor using fuzzy recursive least squares technique

    Directory of Open Access Journals (Sweden)

    Santiago Sánchez

    2008-12-01

    Full Text Available A simple adaptive controller design is presented in this paper, the control system uses the adaptive fuzzy logic, sliding modes and is trained with the recursive least squares technique. The problem of parameter variation is solved with the adaptive controller; the use of an internal PI regulator produces that the speed control of the induction motor be achieved by the stator currents instead the input voltage. The rotor-flux oriented coordinated system model is used to develop and test the control system.

  5. A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation

    Directory of Open Access Journals (Sweden)

    Cinthia Peraza

    2016-10-01

    Full Text Available In this paper, a new fuzzy harmony search algorithm (FHS for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR and pitch adjustment (PArate parameters that improve the convergence rate of traditional harmony search algorithm (HS. The objective of the method is to dynamically adjust the parameters in the range from 0.7 to 1. The impact of using fixed parameters in the harmony search algorithm is discussed and a strategy for efficiently tuning these parameters using fuzzy logic is presented. The FHS algorithm was successfully applied to different benchmarking optimization problems. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.

  6. Study on Fuzzy Adaptive Fractional Order PIλDμ Control for Maglev Guiding System

    Science.gov (United States)

    Hu, Qing; Hu, Yuwei

    The mathematical model of the linear elevator maglev guiding system is analyzed in this paper. For the linear elevator needs strong stability and robustness to run, the integer order PID was expanded to the fractional order, in order to improve the steady state precision, rapidity and robustness of the system, enhance the accuracy of the parameter in fractional order PIλDμ controller, the fuzzy control is combined with the fractional order PIλDμ control, using the fuzzy logic achieves the parameters online adjustment. The simulations reveal that the system has faster response speed, higher tracking precision, and has stronger robustness to the disturbance.

  7. Adaptive neuro-fuzzy inference system based automatic generation control

    Energy Technology Data Exchange (ETDEWEB)

    Hosseini, S.H.; Etemadi, A.H. [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran)

    2008-07-15

    Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the proposed method is demonstrated via simulations. Compliance of the proposed method with NERC control performance standard is verified. (author)

  8. Fuzzy control in environmental engineering

    CERN Document Server

    Chmielowski, Wojciech Z

    2016-01-01

    This book is intended for engineers, technicians and people who plan to use fuzzy control in more or less developed and advanced control systems for manufacturing processes, or directly for executive equipment. Assuming that the reader possesses elementary knowledge regarding fuzzy sets and fuzzy control, by way of a reminder, the first parts of the book contain a reminder of the theoretical foundations as well as a description of the tools to be found in the Matlab/Simulink environment in the form of a toolbox. The major part of the book presents applications for fuzzy controllers in control systems for various manufacturing and engineering processes. It presents seven processes and problems which have been programmed using fuzzy controllers. The issues discussed concern the field of Environmental Engineering. Examples are the control of a flood wave passing through a hypothetical, and then the real Dobczyce reservoir in the Raba River, which is located in the upper Vistula River basin in Southern Poland, th...

  9. Adaptive fuzzy control of neutron power of the TRIGA Mark III reactor; Control difuso adaptable de la potencia neutronica del reactor Triga Mark III

    Energy Technology Data Exchange (ETDEWEB)

    Rojas R, E.

    2014-07-01

    The design and implementation of an identification and control scheme of the TRIGA Mark III research nuclear reactor of the Instituto Nacional de Investigaciones Nucleares (ININ) of Mexico is presented in this thesis work. The identification of the reactor dynamics is carried out using fuzzy logic based systems, in which a learning process permits the adjustment of the membership function parameters by means of techniques based on neural networks and bio-inspired algorithms. The resulting identification system is a useful tool that allows the emulation of the reactor power behavior when different types of insertions of reactivity are applied into the core. The identification of the power can also be used for the tuning of the parameters of a control system. On the other hand, the regulation of the reactor power is carried out by means of an adaptive and stable fuzzy control scheme. The control law is derived using the input-output linearization technique, which permits the introduction of a desired power profile for the plant to follow asymptotically. This characteristic is suitable for managing the ascent of power from an initial level n{sub o} up to a predetermined final level n{sub f}. During the increase of power, a constraint related to the rate of change in power is considered by the control scheme, thus minimizing the occurrence of a safety reactor shutdown due to a low reactor period value. Furthermore, the theory of stability in the sense of Lyapunov is used to obtain a supervisory control law which maintains the power error within a tolerance region, thus guaranteeing the stability of the power of the closed loop system. (Author)

  10. An Adaptive Fuzzy Control Scheme for Dyebath pH in Exhaust Dyeing

    African Journals Online (AJOL)

    Nafiisah

    parameters such as temperature, salt concentration, the quality of the dyed fabric, .... where the natural angular frequency, n = 0.337 rad. ... (6). Dosing pumps and dye bath. The control signal generated by the fuzzy controller, u(kT), determines ...

  11. Fuzzy control of pressurizer dynamic process

    International Nuclear Information System (INIS)

    Ming Zhedong; Zhao Fuyu

    2006-01-01

    Considering the characteristics of pressurizer dynamic process, the fuzzy control system that takes the advantages of both fuzzy controller and PID controller is designed for the dynamic process in pressurizer. The simulation results illustrate this type of composite control system is with better qualities than those of single fuzzy controller and single PID controller. (authors)

  12. Intelligent control-III: fuzzy control system

    International Nuclear Information System (INIS)

    Nagrial, M.H.

    2004-01-01

    During the last decade or so, fuzzy logic control (FLC) has emerged as one of the most active and fruitful areas of research and development. The applications include industrial process control to medical diagnostic and financial markets. Many consumer products using this technology are available in the market place. FLC is best suited to complex ill-defined processes that can be controlled by a skilled human operator without much knowledge of their underlying dynamics. This lecture will cover the basic architecture and the design methodology of fuzzy logic controllers. FLC will be strongly based on the concepts of fuzzy set theory, introduced in first lecture. Some practical applications will also be discussed and presented. (author)

  13. Designing PID-Fuzzy Controller for Pendubot System

    Directory of Open Access Journals (Sweden)

    Ho Trong Nguyen

    2017-12-01

    Full Text Available In the paper, authors analize dynamic equation of a pendubot system. Familiar kinds of controller – PID, fuzzy controllers – are concerned. Then, a structure of PID-FUZZY is presented. The comparison of three kinds of controllers – PID, fuzzy and PID-FUZZY shows the better response of system under PID-FUZZY controller. Then, the experiments on the real model also prove the better stabilization of the hybrid controller which is combined between linear and intelligent controller.

  14. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  15. A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

    Directory of Open Access Journals (Sweden)

    S. M. Odeh

    2015-01-01

    Full Text Available This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC and Genetic Algorithms (GAs and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC, and up to 31% in the comparison with a traditional logic controller, FLC.

  16. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    Peng Yafu; Hsu, C.-F.

    2009-01-01

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

  17. New fuzzy EWMA control charts for monitoring phase II fuzzy profiles

    Directory of Open Access Journals (Sweden)

    Ghazale Moghadam

    2016-01-01

    Full Text Available In many quality control applications, the quality of a process or product is explained by the relationship between response variable and one or more explanatory variables, called a profile. In this paper, a new fuzzy EWMA control chart for phase II fuzzy profile monitoring is proposed. To this end, we extend EWMA control charts to its equivalent Fuzzy type and then implement fuzzy ranking methods to determine whether the process fuzzy profile is under or out of control. The proposed method is capable of identifying small changes in process under condition of process profile explaining parameters vagueness, roughness and uncertainty. Determining the source of changes, this method provides us with the possibility of recognizing the causes of process transition from stable mode, removing these causes and restoring the process stable mode.

  18. Optimizing pipeline transportation using a fuzzy controller

    Energy Technology Data Exchange (ETDEWEB)

    Aramaki, Thiago L.; Correa, Joao L. L.; Montalvoa, Antonio F. F. [National Control and Operation Center Tranpetro, Rio de Janeiro, (Brazil)

    2010-07-01

    The optimization of pipeline transportation is a big concern for the transporter companies. This paper is the third of a series of three articles which investigated the application of a system to simulate the human ability to operate a pipeline in an optimized way. The present paper presents the development of a proportional integral (PI) fuzzy controller, in order to optimize pipeline transportation capacity. The fuzzy adaptive PI controller system was developed and tested with a hydraulic simulator. On-field data were used from the OSBRA pipeline. The preliminary tests showed that the performance of the software simulation was satisfactory. It varied the set-point of the conventional controller within the limits of flow meters. The transport capacity of the pipe was maximize without compromising the integrity of the commodities transported. The system developed proved that it can be easily deployed as a specialist optimizing system to be added to SCADA systems.

  19. Improved Fuzzy Logic based DTC of Induction machine for wide range of speed control using AI based controllers

    Directory of Open Access Journals (Sweden)

    H. Sudheer

    2016-06-01

    Full Text Available This paper presents improvements in Direct Torque control of induction motor using Fuzzy logic switching controller (FDTC. The conventional DTC (CDTC and FDTC drive performance is compared using Conventional PI, Fuzzy controller and Neural Network controllers. The major disadvantages of CDTC are high torque and flux ripples in steady state operation of the drive, inferior performance at low speed operation and variable switching frequency. The presence of hysteresis bands is the major reason for high torque and flux ripples in CDTC. In FDTC the hysteresis band and switching table are replaced by Fuzzy logic switching controller. Using fuzzy logic torque, stator flux space are divided into smaller subsections which results in precise and optimal selection of switching state to meet load torque. In high performance drives accurate tuning of PI speed controller is required. The conventional PI controller cannot adapt to the variation in model parameters. Artificial intelligence based fuzzy controller and neural network controller are compared with PI controller for both CDTC and FDTC of Induction machine. The proposed schemes are developed in Matlab/Simulink environment. Simulation results shows reduction in torque and flux ripples in FDTC and dynamic performance of the drive at low speeds and sudden change in load torque can be improved using Fuzzy logic controller compared to PI and neural network controller.

  20. Adaptive fuzzy observer-based stabilization of a class of uncertain time-delayed chaotic systems with actuator nonlinearities

    International Nuclear Information System (INIS)

    Shahnazi, Reza; Haghani, Adel; Jeinsch, Torsten

    2015-01-01

    An observer-based output feedback adaptive fuzzy controller is proposed to stabilize a class of uncertain chaotic systems with unknown time-varying time delays, unknown actuator nonlinearities and unknown external disturbances. The actuator nonlinearity can be backlash-like hysteresis or dead-zone. Based on universal approximation property of fuzzy systems the unknown nonlinear functions are approximated by fuzzy systems, where the consequent parts of fuzzy rules are tuned with adaptive schemes. The proposed method does not need the availability of the states and an observer based output feedback approach is proposed to estimate the states. To have more robustness and at the same time to alleviate chattering an adaptive discontinuous structure is suggested. Semi-global asymptotic stability of the overall system is ensured by proposing a suitable Lyapunov–Krasovskii functional candidate. The approach is applied to stabilize the time-delayed Lorenz chaotic system with uncertain dynamics amid significant disturbances. Analysis of simulations reveals the effectiveness of the proposed method in terms of coping well with the modeling uncertainties, nonlinearities in actuators, unknown time-varying time-delays and unknown external disturbances while maintaining asymptotic convergence

  1. Fuzzy control for optimal operation of complex chilling systems; Betriebsoptimierung von komplexen Kaelteanlagen mit Fuzzy-Control

    Energy Technology Data Exchange (ETDEWEB)

    Talebi-Daryani, R. [Fachhochschule Koeln (Germany). Lehrgebiet und Lab. fuer Regelungs- und Gebaeudeleittechnik; Luther, C. [JCI Regelungstechnik GmbH, Koeln (Germany)

    1998-05-01

    The optimization potentials for the operation of chilling systems within the building supervisory control systems are limited to abilities of PLC functions with their binary logic. The aim of this project is to replace inefficient PLC-solutions for the operation of chilling system by a Fuzzy control system. Optimal operation means: reducing operation time and operation costs of the system, reducing cooling energy generation- and consumption costs. Analysis of the thermal behaviour of the building and the chilling system is necessary, in order to find the current efficient cooling potentials and cooling methods during the operation. Three different Fuzzy controller have been developed with a total rule number of just 70. This realized Fuzzy control system is able to forecast the maximum cooling power of the building, but also to determine the cooling potential of the out door air. This new Fuzzy control system has been successfully commissioned, and remarkable improvement of the system behaviour is reached. Comparison of the system behaviour before and after the implementation of Fuzzy control system proved the benefits of the Fuzzy logic based operation system realized here. The system described here is a joint project between the University of applied sciences Cologne, and Johnson Controls International Cologne. The Fuzzy software tool used here (SUCO soft Fuzzy TECH 4.0), was provided by Kloeckner Moeller Bonn. (orig.) [Deutsch] Die Betriebsoptimierung von Kaelteanlagen innerhalb von Gebaeudeleitsystemen ist auf die Faehigkeiten von logischen Steuerverknuepfungen der Digitaltechnik begrenzt. In diesem Zusammenhang kann nur ein geringer Anteil der Information ueber das thermische Speicherverhalten des jeweiligen Gebaeudes herangezogen werden. Ziel des vorliegenden Projektes war es, die unzureichenden logischen Steuerverknuepfungen durch ein Fuzzy-Control-System zu ersetzen, um die Arbeitsweise der Kaelteanlage zu optimieren. Die Optimierungskriterien dieses

  2. Fuzzy logic control and optimization system

    Science.gov (United States)

    Lou, Xinsheng [West Hartford, CT

    2012-04-17

    A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

  3. Exploiting maximum energy from variable speed wind power generation systems by using an adaptive Takagi-Sugeno-Kang fuzzy model

    International Nuclear Information System (INIS)

    Galdi, V.; Piccolo, A.; Siano, P.

    2009-01-01

    Nowadays, incentives and financing options for developing renewable energy facilities and the new development in variable speed wind technology make wind energy a competitive source if compared with conventional generation ones. In order to improve the effectiveness of variable speed wind systems, adaptive control systems able to cope with time variances of the system under control are necessary. On these basis, a data driven designing methodology for TSK fuzzy models design is presented in this paper. The methodology, on the basis of given input-output numerical data, generates the 'best' TSK fuzzy model able to estimate with high accuracy the maximum extractable power from a variable speed wind turbine. The design methodology is based on fuzzy clustering methods for partitioning the input-output space combined with genetic algorithms (GA), and recursive least-squares (LS) optimization methods for model parameter adaptation

  4. Auto-adaptative Robot-aided Therapy based in 3D Virtual Tasks controlled by a Supervised and Dynamic Neuro-Fuzzy System

    Directory of Open Access Journals (Sweden)

    Luis Daniel Lledó

    2015-03-01

    Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.

  5. Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control

    International Nuclear Information System (INIS)

    Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel

    2014-01-01

    Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller’s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers. (paper)

  6. Path following of an Underactuated AUV Based on Fuzzy Backstepping Sliding Mode Control

    Directory of Open Access Journals (Sweden)

    Xiao Liang

    2016-06-01

    Full Text Available This paper addresses the path following problem of an underactuated autonomous underwater vehicle (AUV with the aim of dealing with parameter uncertainties and current disturbances. An adaptive robust control system was proposed by employing fuzzy logic, backstepping and sliding mode control theory. Fuzzy logic theory is adopted to approximate unknown system function, and the controller was designed by combining sliding mode control with backstepping thought. Firstly, the longitudinal speed was controlled, then the yaw angle was made as input of path following error to design the calm function and the change rate of path parameters. The controller stability was proved by Lyapunov stable theory. Simulation and outfield tests were conducted and the results showed that the controller is of excellent adaptability and robustness in the presence of parameter uncertainties and external disturbances. It is also shown to be able to avoid the chattering of AUV actuators.

  7. Fuzzy logic controller using different inference methods

    International Nuclear Information System (INIS)

    Liu, Z.; De Keyser, R.

    1994-01-01

    In this paper the design of fuzzy controllers by using different inference methods is introduced. Configuration of the fuzzy controllers includes a general rule-base which is a collection of fuzzy PI or PD rules, the triangular fuzzy data model and a centre of gravity defuzzification algorithm. The generalized modus ponens (GMP) is used with the minimum operator of the triangular norm. Under the sup-min inference rule, six fuzzy implication operators are employed to calculate the fuzzy look-up tables for each rule base. The performance is tested in simulated systems with MATLAB/SIMULINK. Results show the effects of using the fuzzy controllers with different inference methods and applied to different test processes

  8. Adaptive fuzzy observer based synchronization design and secure communications of chaotic systems

    International Nuclear Information System (INIS)

    Hyun, Chang-Ho; Kim, Jae-Hun; Kim, Euntai; Park, Mignon

    2006-01-01

    This paper proposes a synchronization design scheme based on an alternative indirect adaptive fuzzy observer and its application to secure communication of chaotic systems. It is assumed that their states are unmeasurable and their parameters are unknown. Chaotic systems and the structure of the fuzzy observer are represented by the Takagi-Sugeno fuzzy model. Using Lyapunov stability theory, an adaptive law is derived to estimate the unknown parameters and the stability of the proposed system is guaranteed. Through this process, the asymptotic synchronization of chaotic systems is achieved. The proposed observer is applied to secure communications of chaotic systems and some numerical simulation results show the validity of theoretical derivations and the performance of the proposed observer

  9. Application of a PID controller based on fuzzy logic to reduce variations in the control parameters in PWR reactors

    International Nuclear Information System (INIS)

    Vasconcelos, Wagner Eustaquio de; Lira, Carlos Alberto Brayner de Oliveira; Brito, Thiago Souza Pereira de; Afonso, Antonio Claudio Marques; Cruz Filho, Antonio Jose da; Marques, Jose Antonio; Teixeira, Marcello Goulart

    2013-01-01

    Nuclear reactors are in nature nonlinear systems and their parameters vary with time as a function of power level. These characteristics must be considered if large power variations occur in power plant operational regimes, such as in load-following conditions. A PWR reactor has a component called pressurizer, whose function is to supply the necessary high pressure for its operation and to contain pressure variations in the primary cooling system. The use of control systems capable of reducing fast variations of the operation variables and to maintain the stability of this system is of fundamental importance. The best-known controllers used in industrial control processes are proportional-integral-derivative (PID) controllers due to their simple structure and robust performance in a wide range of operating conditions. However, designing a fuzzy controller is seen to be a much less difficult task. Once a Fuzzy Logic controller is designed for a particular set of parameters of the nonlinear element, it yields satisfactory performance for a range of these parameters. The objective of this work is to develop fuzzy proportional-integral-derivative (fuzzy-PID) control strategies to control the level of water in the reactor. In the study of the pressurizer, several computer codes are used to simulate its dynamic behavior. At the fuzzy-PID control strategy, the fuzzy logic controller is exploited to extend the finite sets of PID gains to the possible combinations of PID gains in stable region. Thus the fuzzy logic controller tunes the gain of PID controller to adapt the model with changes in the water level of reactor. The simulation results showed a favorable performance with the use to fuzzy-PID controllers. (author)

  10. A SELF-ORGANISING FUZZY LOGIC CONTROLLER

    African Journals Online (AJOL)

    ES Obe

    One major drawback of fuzzy logic controllers is the difficulty encountered in the construction of a rule- base ... The greatest limitation of fuzzy logic control is the lack ..... c(kT)= e(kT)-e((k-1)T). (16) .... with the aid of fuzzy models”, It in Industrial.

  11. Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

    Science.gov (United States)

    Wang, Wei-Cheng; Tai, Cheng-Chi

    2017-07-01

    The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

  12. Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

    Science.gov (United States)

    Lam, H K

    2012-02-01

    This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

  13. ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR END MILLING

    Directory of Open Access Journals (Sweden)

    ANGELOS P. MARKOPOULOS

    2016-09-01

    Full Text Available Soft computing is commonly used as a modelling method in various technological areas. Methods such as Artificial Neural Networks and Fuzzy Logic have found application in manufacturing technology as well. NeuroFuzzy systems, aimed to combine the benefits of both the aforementioned Artificial Intelligence methods, are a subject of research lately as have proven to be superior compared to other methods. In this paper an adaptive neuro-fuzzy inference system for the prediction of surface roughness in end milling is presented. Spindle speed, feed rate, depth of cut and vibrations were used as independent input variables, while roughness parameter Ra as dependent output variable. Several variations are tested and the results of the optimum system are presented. Final results indicate that the proposed model can accurately predict surface roughness, even for input that was not used in training.

  14. Fuzzy logic control of nuclear power plant

    International Nuclear Information System (INIS)

    Yao Liangzhong; Guo Renjun; Ma Changwen

    1996-01-01

    The main advantage of the fuzzy logic control is that the method does not require a detailed mathematical model of the object to be controlled. In this paper, the shortcomings and limitations of the model-based method in nuclear power plant control were presented, the theory of the fuzzy logic control was briefly introduced, and the applications of the fuzzy logic control technology in nuclear power plant controls were surveyed. Finally, the problems to be solved by using the fuzzy logic control in nuclear power plants were discussed

  15. Application of fuzzy logic control in industry

    International Nuclear Information System (INIS)

    Van der Wal, A.J.

    1994-01-01

    An overview is given of the various ways fuzzy logic can be used to improve industrial control. The application of fuzzy logic in control is illustrated by two case studies. The first example shows how fuzzy logic, incorporated in the hardware of an industrial controller, helps to finetune a PID controller, without the operator having any a priori knowledge of the system to be controlled. The second example is from process industry. Here, fuzzy logic supervisory control is implemented in software and enhances the operation of a sintering oven through a subtle combination of priority management and deviation-controlled timing

  16. Asymmetric Fuzzy Control of a Positive and Negative Pneumatic Pressure Servo System

    Science.gov (United States)

    Yang, Gang; Du, Jing-Min; Fu, Xiao-Yun; Li, Bao-Ren

    2017-11-01

    The pneumatic pressure control systems have been used in some fields. However, the researches on pneumatic pressure control mainly focus on constant pressure regulation. Poor dynamic characteristics and strong nonlinearity of such systems limit its application in the field of pressure tracking control. In order to meet the demand of generating dynamic pressure signal in the application of the hardware-in-the-loop simulation of aerospace engineering, a positive and negative pneumatic pressure servo system is provided to implement dynamic adjustment of sealed chamber pressure. A mathematical model is established with simulation and experiment being implemented afterwards to discuss the characteristics of the system, which shows serious asymmetry in the process of charging and discharging. Based on the analysis of the system dynamics, a fuzzy proportional integral derivative (PID) controller with asymmetric fuzzy compensator is proposed. Different from conventional adjusting mechanisms employing the error and change in error of the controlled variable as input parameters, the current chamber pressure and charging or discharging state are chosen as inputs of the compensator, which improves adaptability. To verify the effectiveness and performance of the proposed controller, the comparison experiments tracking sinusoidal and square wave commands are conducted. Experimental results show that the proposed controller can obtain better dynamic performance and relatively consistent control performance across the scope of work (2-140 kPa). The research proposes a fuzzy control method to overcome asymmetry and enhance adaptability for the positive and negative pneumatic pressure servo system.

  17. Improved Transient Performance of a Fuzzy Modified Model Reference Adaptive Controller for an Interacting Coupled Tank System Using Real-Coded Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Asan Mohideen Khansadurai

    2014-01-01

    Full Text Available The main objective of the paper is to design a model reference adaptive controller (MRAC with improved transient performance. A modification to the standard direct MRAC called fuzzy modified MRAC (FMRAC is used in the paper. The FMRAC uses a proportional control based Mamdani-type fuzzy logic controller (MFLC to improve the transient performance of a direct MRAC. The paper proposes the application of real-coded genetic algorithm (RGA to tune the membership function parameters of the proposed FMRAC offline so that the transient performance of the FMRAC is improved further. In this study, a GA based modified MRAC (GAMMRAC, an FMRAC, and a GA based FMRAC (GAFMRAC are designed for a coupled tank setup in a hybrid tank process and their transient performances are compared. The results show that the proposed GAFMRAC gives a better transient performance than the GAMMRAC or the FMRAC. It is concluded that the proposed controller can be used to obtain very good transient performance for the control of nonlinear processes.

  18. Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

    Directory of Open Access Journals (Sweden)

    Ahcene Farah

    2002-06-01

    Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

  19. Fuzzy logic for structural system control

    Directory of Open Access Journals (Sweden)

    Herbert Martins Gomes

    Full Text Available This paper provides some information and numerical tests that aims to investigate the use of a Fuzzy Controller applied to control systems. Some advantages are reported regarding the use of this controller, such as the characteristic ease of implementation due to its semantic feature in the statement of the control rules. On the other hand, it is also hypothesized that these systems have a lower performance loss when the system to be controlled is nonlinear or has time varying parameters. Numerical tests are performed using modal LQR optimal control and Fuzzy control of non-collocated systems with full state feedback in a two-dimensional structure. The paper proposes a way of designing a controller that may be a supervisory Fuzzy controller for a traditional controller or even a fuzzy controller independent from the traditional control, consisting on individual mode controllers. Some comments are drawn regarding the performance of these proposals in a number of arrangements.

  20. Esophageal cancer prediction based on qualitative features using adaptive fuzzy reasoning method

    Directory of Open Access Journals (Sweden)

    Raed I. Hamed

    2015-04-01

    Full Text Available Esophageal cancer is one of the most common cancers world-wide and also the most common cause of cancer death. In this paper, we present an adaptive fuzzy reasoning algorithm for rule-based systems using fuzzy Petri nets (FPNs, where the fuzzy production rules are represented by FPN. We developed an adaptive fuzzy Petri net (AFPN reasoning algorithm as a prognostic system to predict the outcome for esophageal cancer based on the serum concentrations of C-reactive protein and albumin as a set of input variables. The system can perform fuzzy reasoning automatically to evaluate the degree of truth of the proposition representing the risk degree value with a weight value to be optimally tuned based on the observed data. In addition, the implementation process for esophageal cancer prediction is fuzzily deducted by the AFPN algorithm. Performance of the composite model is evaluated through a set of experiments. Simulations and experimental results demonstrate the effectiveness and performance of the proposed algorithms. A comparison of the predictive performance of AFPN models with other methods and the analysis of the curve showed the same results with an intuitive behavior of AFPN models.

  1. PSO based neuro fuzzy sliding mode control for a robot manipulator

    Directory of Open Access Journals (Sweden)

    M. Vijay

    2017-05-01

    Full Text Available This paper presents the control strategy of two degrees of freedom (2DOF rigid robot manipulator based on the coupling of artificial neuro fuzzy inference system (ANFIS with sliding mode control (SMC. Initially SMC with proportional integral derivative (PID sliding surface is adapted to control the robot manipulator. The parameters of the sliding surface are obtained by minimizing a quadratic performance indices using particle swarm optimization (PSO. Variations of SMC i.e. boundary sliding mode control (BSMC and boundary sliding mode control with PID sliding surface (PIDBSMC are developed for optimized performance index. Finally an ANFIS adaptive controller is proposed to generate the adaptive control signal and found to be more robust with regard to disturbances in input torque.

  2. Robust position control of induction motor using fuzzy logic control

    International Nuclear Information System (INIS)

    Kim, Sei Chan; Kim, Duk Hun; Yang, Seung Ho; Won, Chung Yuen

    1993-01-01

    In recent years, fuzzy logic or fuzzy set theory has reveived attention of a number of researchers in the area of power electronics and motion control. The paper describes a vector-controlled induction motor position servo drive where fuzzy control is used to get robustness against parameter variation and load torque disturbance effects. Both coarse and fine control with the help of look-up rule tables are used to improve transient response and system settling time. The performance characteristics are then compared with those of proportional-integral(PI) control. The simulation results clearly indicate the superiority of fuzzy control with larger number of rules. The fuzzy controller was implemented with a 16-bit microprocessor and tested in laboratory on a 3-hp IGBT inverter induction motor drive system. The test results verify the simulation performance. (Author)

  3. Control-technical optimization of topped denitrification through fuzzy control; Regelungstechnische Optimierung der vorgeschalteten Denitrifikation durch Anwendung von Fuzzy-Control

    Energy Technology Data Exchange (ETDEWEB)

    Meyer, U. [Hydroplan Ingenieurgesellschaft, Worms (Germany); Poepel, H.J. [Technische Univ. Darmstadt (Germany). Inst. WAR - Wasserversorgung, Abwassertechnik, Abfalltechnik, Umwelt- und Raumplanung

    1999-07-01

    The present paper describes complex fuzzy systems for controlling nitrogen elimination at plants with topped denitrification. For the design and testing of the fuzzy systems, dynamic simulation calculations and experimental tests were carried out in a semi-technical pilot plant. The controlling fuzzy systems indicate the supposed oxygen values for individual tank areas and the most appropriate partitioning of the activated sludge tank (anoxic/aerobic zone) as a function of the input quantities used. It is established that, with the more flexible control behaviour, a more stable nitrogen elimination and, at the same time, a cut in the amount of air transferred to the system can be attained in comparison with a conventional control. (orig.) [German] Im Rahmen dieses Beitrags werden komplexe Fuzzy-Systeme zur Regelung der Stickstoffelimination in Anlagen mit vorgeschalteter Denitrifikation vorgestellt. Zum Entwurf und zur Erprobung der Fuzzy-Systeme wurden dynamische Simulationsrechnungen und experimentelle Untersuchungen an einer halbtechnischen Versuchsanlage durchgefuehrt. Die uebergeordneten Fuzzy-Systeme geben in Abhaengigkeit der verwendeten Eingangsgroessen Sauerstoffsollwerte fuer die einzelnen Beckenbereiche und die jeweils guenstigste Aufteilung des Belebungsbeckens (anoxische/aerobe Zone) vor. Die Ergebnisse zeigen, dass durch das flexible Reglerverhalten eine stabilere Stickstoffelimination und gleichzeitig eine Einsparung an eingetragener Luftmenge im Vergleich zu einem konventionellen Regelsystem erreicht werden kann. (orig.)

  4. Fuzzy controllers in nuclear material accounting

    International Nuclear Information System (INIS)

    Zardecki, A.

    1994-01-01

    Fuzzy controllers are applied to predicting and modeling a time series, with particular emphasis on anomaly detection in nuclear material inventory differences. As compared to neural networks, the fuzzy controllers can operate in real time; their learning process does not require many iterations to converge. For this reason fuzzy controllers are potentially useful in time series forecasting, where the authors want to detect and identify trends in real time. They describe an object-oriented implementation of the algorithm advanced by Wang and Mendel. Numerical results are presented both for inventory data and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effects of noise on the predictive power of the fuzzy controller are explored

  5. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    Directory of Open Access Journals (Sweden)

    Zhixian Yang

    2014-01-01

    Full Text Available Background electroencephalography (EEG, recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE and sample entropy (SampEn in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

  6. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    Science.gov (United States)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  7. Intelligent control-I: review of fuzzy logic and fuzzy set theory

    International Nuclear Information System (INIS)

    Nagrial, M.H.

    2004-01-01

    In the past decade or so, fuzzy systems have supplanted conventional technologies in many engineering systems, in particular in control systems and pattern recognition. Fuzzy logic has found applications in a variety of consumer products e.g. washing machines, camcorders, digital cameras, air conditioners, subway trains, cement kilns and many others. The fuzzy technology is also being applied in information technology, where it provides decision-support and expert systems with powerful reasoning capabilities. Fuzzy sets, introduced by Zadeh in 1965 as a mathematical way to represent vagueness in linguistics, can be considered a generalisation of classical set theory. Fuzziness is often confused with probability. This lecture will introduce the principal concepts and mathematical notions of fuzzy set theory. (author)

  8. Study on Design of Control Module and Fuzzy Control System

    International Nuclear Information System (INIS)

    Lee, Chang Kyu; Sohn, Chang Ho; Kim, Jung Seon; Kim, Min Kyu

    2005-01-01

    Performance of control unit is improved by introduction of fuzzy control theory and compensation for input of control unit as FLC(Fuzzy Logic Controller). Here, FLC drives thermal control system by linguistic rule-base. Hence, In case of using compensative PID control unit, it doesn't need to revise or compensate for PID control unit. Consequently, this study shows proof that control system which implements H/W module and then uses fuzzy algorism in this system is stable and has reliable performance

  9. A new fuzzy-disturbance observer-enhanced sliding controller for vibration control of a train-car suspension with magneto-rheological dampers

    Science.gov (United States)

    Nguyen, Sy Dzung; Choi, Seung-Bok; Nguyen, Quoc Hung

    2018-05-01

    Semi-active train-car suspensions are always impacted negatively by uncertainty and disturbance (UAD). In order to deal with this, we propose a novel optimal fuzzy disturbance observer-enhanced sliding mode controller (FDO-SMC) for magneto-rheological damper (MRD)-based semi-active train-car suspensions subjected to UAD whose variability rate may be high but bounded. The two main parts of the FDO-SMC are an adaptive sliding mode controller (ad-SMC) and an optimal fuzzy disturbance observer (op-FDO). As the first step, the initial structures of the sliding mode controller (SMC) and disturbance observer (DO) are built. Adaptive update laws for the SMC and DO are then set up synchronously via Lyapunov stability analysis. Subsequently, an optimal fuzzy system (op-FS) is designed to fully implement a parameter constraint mechanism so as to guarantee the system stability converging to the desired state even if the UAD variability rate increases in a given range. As a result, both the ad-SMC and op-FDO are formulated. It is shown from the comparative work with existing controllers that the proposed method provides the best vibration control capability with relatively low consumed power.

  10. Fuzzy combination of fuzzy and switching state-feedback controllers for nonlinear systems subject to parameter uncertainties.

    Science.gov (United States)

    Lam, H K; Leung, Frank H F

    2005-04-01

    This paper presents a fuzzy controller, which involves a fuzzy combination of local fuzzy and global switching state-feedback controllers, for nonlinear systems subject to parameter uncertainties with known bounds. The nonlinear system is represented by a fuzzy combined Takagi-Sugeno-Kang model, which is a fuzzy combination of the global and local fuzzy plant models. By combining the local fuzzy and global switching state-feedback controllers using fuzzy logic techniques, the advantages of both controllers can be retained and the undesirable chattering effect introduced by the global switching state-feedback controller can be eliminated. The steady-state error introduced by the global switching state-feedback controller when a saturation function is used can also be removed. Stability conditions, which are related to the system matrices of the local and global closed-loop systems, are derived to guarantee the closed-loop system stability. An application example will be given to demonstrate the merits of the proposed approach.

  11. Simulation of Fuzzy Adaptive PI Controlled Grid Interactive Inverter

    Directory of Open Access Journals (Sweden)

    Necmi ALTIN

    2009-03-01

    Full Text Available In this study, a voltage source grid interactive inverter is modeled and simulated in MATLAB/Simulink. Inverter is designed as current controlled and a fuzzy-PI current controller used for the generation of switching pattern to shape the inverter output current. The grid interactive inverter consists of a line frequency transformer and a LC type filter. Galvanic isolation between the grid and renewable energy source is obtained by the line frequency transformer and LC filter is employed to filter the high frequency harmonic components in current waveform due to PWM switching and to reduce the output current THD. Results of the MATLAB/Simulink simulation show that inverter output current is in sinusoidal waveform and in phase with line voltage, and current harmonics are in the limits of international standards (

  12. Analysis of inventory difference using fuzzy controllers

    International Nuclear Information System (INIS)

    Zardecki, A.

    1994-01-01

    The principal objectives of an accounting system for safeguarding nuclear materials are as follows: (a) to provide assurance that all material quantities are present in the correct amount; (b) to provide timely detection of material loss; and (c) to estimate the amount of any loss and its location. In fuzzy control, expert knowledge is encoded in the form of fuzzy rules, which describe recommended actions for different classes of situations represented by fuzzy sets. The concept of a fuzzy controller is applied to the forecasting problem in a time series, specifically, to forecasting and detecting anomalies in inventory differences. This paper reviews the basic notion underlying the fuzzy control systems and provides examples of application. The well-known material-unaccounted-for diffusion plant data of Jaech are analyzed using both feedforward neural networks and fuzzy controllers. By forming a deference between the forecasted and observed signals, an efficient method to detect small signals in background noise is implemented

  13. Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management

    Science.gov (United States)

    Chang, N.

    2006-12-01

    The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals

  14. A study of fuzzy control in nuclear scale system

    International Nuclear Information System (INIS)

    Wang Yu; Zhang Yongming; Wu Ruisheng; Du Xianbin; Liu Shixing

    2001-01-01

    The new development of the nuclear scale system which uses fuzzy control strategy is presented. Good results have been obtained in using fuzzy control to solve the problems, such as un-linearities, instabilities, time delays, which are difficultly described by formula, etc. The fuzzy variance, membership function and fuzzy rules are given, and the noise disturbances of fuzzy control and PID control are also given

  15. Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

    Science.gov (United States)

    Ibrahim, Wael Refaat Anis

    The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an

  16. Switched Two-Level H∞ and Robust Fuzzy Learning Control of an Overhead Crane

    Directory of Open Access Journals (Sweden)

    Kao-Ting Hung

    2013-01-01

    Full Text Available Overhead cranes are typical dynamic systems which can be modeled as a combination of a nominal linear part and a highly nonlinear part. For such kind of systems, we propose a control scheme that deals with each part separately, yet ensures global Lyapunov stability. The former part is readily controllable by the H∞ PDC techniques, and the latter part is compensated by fuzzy mixture of affine constants, leaving the remaining unmodeled dynamics or modeling error under robust learning control using the Nelder-Mead simplex algorithm. Comparison with the adaptive fuzzy control method is given via simulation studies, and the validity of the proposed control scheme is demonstrated by experiments on a prototype crane system.

  17. Wide-range nuclear reactor temperature control using automatically tuned fuzzy logic controller

    International Nuclear Information System (INIS)

    Ramaswamy, P.; Edwards, R.M.; Lee, K.Y.

    1992-01-01

    In this paper, a fuzzy logic controller design for optimal reactor temperature control is presented. Since fuzzy logic controllers rely on an expert's knowledge of the process, they are hard to optimize. An optimal controller is used in this paper as a reference model, and a Kalman filter is used to automatically determine the rules for the fuzzy logic controller. To demonstrate the robustness of this design, a nonlinear six-delayed-neutron-group plant is controlled using a fuzzy logic controller that utilizes estimated reactor temperatures from a one-delayed-neutron-group observer. The fuzzy logic controller displayed good stability and performance robustness characteristics for a wide range of operation

  18. Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems

    Energy Technology Data Exchange (ETDEWEB)

    Jassar, S.; Zhao, L. [Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON (Canada); Liao, Z. [Department of Architectural Science, Ryerson University (Canada)

    2009-08-15

    The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least-square method and the back-propagation algorithm, is used to identify the parameters of the network. This paper describes an adaptive network based inferential sensor that can be used to design closed-loop control for space heating systems. The research aims to improve the overall performance of heating systems, in terms of energy efficiency and thermal comfort. The average air temperature results estimated by using the developed model are strongly in agreement with the experimental results. (author)

  19. Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

    Directory of Open Access Journals (Sweden)

    Adel Taieb

    2017-01-01

    Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.

  20. Conventional control and fuzzy control of a dc-dc converter for machine drive

    Energy Technology Data Exchange (ETDEWEB)

    Radoi, C.; Florescu, A. [Department of Power Electronics `Politecnica` University Bucharest (Romania)

    1997-12-31

    Fuzzy logic or fuzzy set theory is recently getting increasing emphasis in process control applications. The paper describes an application of fuzzy logic in speed control system that uses a dc-dc converter. The fuzzy control is used to linearize the family of external characteristics of the converter in discontinuous-conduction mode occurring at light load and/or high speed. In order to compare the conventional control with the fuzzy logic control, algorithms have been developed in detail and verified by Microsoft Excel simulation. The simulation study indicates that fuzzy control is a good alternative for conventional control methods, being used particularly in non-linear complex systems ill defined or totally unknown. Where the mathematical model exists, it is useful. The applications of fuzzy set theory in power electronics are almost entirely new; fuzzy logic seems to have a lot of premises in the large industrial control field. (orig.) 2 refs.

  1. Introduction to Fuzzy Set Theory

    Science.gov (United States)

    Kosko, Bart

    1990-01-01

    An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

  2. Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources

    Directory of Open Access Journals (Sweden)

    Otilia Elena Dragomir

    2015-11-01

    Full Text Available The challenge for our paper consists in controlling the performance of the future state of a microgrid with energy produced from renewable energy sources. The added value of this proposal consists in identifying the most used criteria, related to each modeling step, able to lead us to an optimal neural network forecasting tool. In order to underline the effects of users’ decision making on the forecasting performance, in the second part of the article, two Adaptive Neuro-Fuzzy Inference System (ANFIS models are tested and evaluated. Several scenarios are built by changing: the prediction time horizon (Scenario 1 and the shape of membership functions (Scenario 2.

  3. Implementation of fuzzy logic control algorithm in embedded ...

    African Journals Online (AJOL)

    Fuzzy logic control algorithm solves problems that are difficult to address with traditional control techniques. This paper describes an implementation of fuzzy logic control algorithm using inexpensive hardware as well as how to use fuzzy logic to tackle a specific control problem without any special software tools. As a case ...

  4. Fuzzy modeling and control theory and applications

    CERN Document Server

    Matía, Fernando; Jiménez, Emilio

    2014-01-01

    Much work on fuzzy control, covering research, development and applications, has been developed in Europe since the 90's. Nevertheless, the existing books in the field are compilations of articles without interconnection or logical structure or they express the personal point of view of the author. This book compiles the developments of researchers with demonstrated experience in the field of fuzzy control following a logic structure and a unified the style. The first chapters of the book are dedicated to the introduction of the main fuzzy logic techniques, where the following chapters focus on concrete applications. This book is supported by the EUSFLAT and CEA-IFAC societies, which include a large number of researchers in the field of fuzzy logic and control. The central topic of the book, Fuzzy Control, is one of the main research and development lines covered by these associations.

  5. interval type-2 fuzzy gain-adaptive controller of a doubly fed

    African Journals Online (AJOL)

    Loukal K and Benalia L

    2016-05-01

    May 1, 2016 ... machine a converter PWM (Pulse Width Modulation) between the machine ... operations on fuzzy sets increases with the increasing type of the fuzzy set. ...... Several simulations have been run using the Matlab and Simulink® ...

  6. Decentralized fuzzy control of multiple nonholonomic vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Driessen, B.J.; Feddema, J.T.; Kwok, K.S.

    1997-09-01

    This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on simple 8-bit microcontrollers, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the (noisy) direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: Forward, Behind, Left, and Right, and the distance into three values: Near, Far, Gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and the change of variables that reduces the motion control problem of each nonholonomic vehicle to a nonsingular one with two degrees of freedom, instead of three. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.

  7. AN INTELLIGENT NEURO-FUZZY TERMINAL SLIDING MODE CONTROL METHOD WITH APPLICATION TO ATOMIC FORCE MICROSCOPE

    Directory of Open Access Journals (Sweden)

    Seied Yasser Nikoo

    2016-11-01

    Full Text Available In this paper, a neuro-fuzzy fast terminal sliding mode control method is proposed for controlling a class of nonlinear systems with bounded uncertainties and disturbances. In this method, a nonlinear terminal sliding surface is firstly designed. Then, this sliding surface is considered as input for an adaptive neuro-fuzzy inference system which is the main controller. A proportinal-integral-derivative controller is also used to asist the neuro-fuzzy controller in order to improve the performance of the system at the begining stage of control operation. In addition, bee algorithm is used in this paper to update the weights of neuro-fuzzy system as well as the parameters of the proportinal-integral-derivative controller. The proposed control scheme is simulated for vibration control in a model of atomic force microscope system and the results are compared with conventional sliding mode controllers. The simulation results show that the chattering effect in the proposed controller is decreased in comparison with the sliding mode and the terminal sliding mode controllers. Also, the method provides the advantages of fast convergence and low model dependency compared to the conventional methods.

  8. Fuzzy logic control for camera tracking system

    Science.gov (United States)

    Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant

    1992-01-01

    A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.

  9. Fuzzy logic control to be conventional method

    International Nuclear Information System (INIS)

    Eker, Ilyas; Torun, Yunis

    2006-01-01

    Increasing demands for flexibility and fast reactions in modern process operation and production methods result in nonlinear system behaviour of partly unknown systems, and this necessitates application of alternative control methods to meet the demands. Fuzzy logic (FL) control can play an important role because knowledge based design rules can easily be implemented in systems with unknown structure, and it is going to be a conventional control method since the control design strategy is simple and practical and is based on linguistic information. Computational complexity is not a limitation any more because the computing power of computers has been significantly improved even for high speed industrial applications. This makes FL control an important alternative method to the conventional PID control method for use in nonlinear industrial systems. This paper presents a practical implementation of the FL control to an electrical drive system. Such drive systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behaviour. For a multi-mass drive system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the operation of the systems. The proposed FL control configuration is based on speed error and change of speed error. The feasibility and effectiveness of the control method are experimentally demonstrated. The results obtained from conventional FL control, fuzzy PID and adaptive FL control are compared with traditional PID control for the dynamic responses of the closed loop drive system

  10. Fuzzy logic control to be conventional method

    Energy Technology Data Exchange (ETDEWEB)

    Eker, Ilyas [University of Gaziantep, Gaziantep (Turkey). Department of Electrical and Electronic Engineering; Torun, Yunis [University of Gaziantep, Gaziantep (Turkey). Technical Vocational School of Higher Education

    2006-03-01

    Increasing demands for flexibility and fast reactions in modern process operation and production methods result in nonlinear system behaviour of partly unknown systems, and this necessitates application of alternative control methods to meet the demands. Fuzzy logic (FL) control can play an important role because knowledge based design rules can easily be implemented in systems with unknown structure, and it is going to be a conventional control method since the control design strategy is simple and practical and is based on linguistic information. Computational complexity is not a limitation any more because the computing power of computers has been significantly improved even for high speed industrial applications. This makes FL control an important alternative method to the conventional PID control method for use in nonlinear industrial systems. This paper presents a practical implementation of the FL control to an electrical drive system. Such drive systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behaviour. For a multi-mass drive system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the operation of the systems. The proposed FL control configuration is based on speed error and change of speed error. The feasibility and effectiveness of the control method are experimentally demonstrated. The results obtained from conventional FL control, fuzzy PID and adaptive FL control are compared with traditional PID control for the dynamic responses of the closed loop drive system. (author)

  11. Optimal fuzzy logic-based PID controller for load-frequency control including superconducting magnetic energy storage units

    International Nuclear Information System (INIS)

    Pothiya, Saravuth; Ngamroo, Issarachai

    2008-01-01

    This paper proposes a new optimal fuzzy logic-based-proportional-integral-derivative (FLPID) controller for load frequency control (LFC) including superconducting magnetic energy storage (SMES) units. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the multiple tabu search (MTS) algorithm is applied to simultaneously tune PID gains, membership functions and control rules of FLPID controller to minimize frequency deviations of the system against load disturbances. The MTS algorithm introduces additional techniques for improvement of search process such as initialization, adaptive search, multiple searches, crossover and restarting process. Simulation results explicitly show that the performance of the optimum FLPID controller is superior to the conventional PID controller and the non-optimum FLPID controller in terms of the overshoot, settling time and robustness against variations of system parameters

  12. Uncovering transcriptional interactions via an adaptive fuzzy logic approach

    Directory of Open Access Journals (Sweden)

    Chen Chung-Ming

    2009-12-01

    Full Text Available Abstract Background To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM performed poorly in inferring transcriptional interactions (TIs, which represent physical interactions between transcription factors (TF and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. Results AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF, and using 19 transcription factors (TFs, we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. Conclusion AdaFuzzy successfully integrates multiple types of

  13. Fuzzy logic controller for weaning neonates from mechanical ventilation.

    Science.gov (United States)

    Hatzakis, G E; Davis, G M

    2002-01-01

    Weaning from mechanical ventilation is the gradual detachment from any ventilatory support till normal spontaneous breathing can be fully resumed. To date, we have developed a fuzzy logic controller for weaning COPD adults using pressure support ventilation (PS). However, adults and newborns differ in the pathophysiology of lung disease. We therefore used our fuzzy logic-based weaning platform to develop modularized components for weaning newborns with lung disease. Our controller uses the heart rate (HR), respiratory rate (RR), tidal volume (VT) and oxygen saturation (SaO2) and their trends deltaHR/deltat, deltaVT/deltat and deltaSaO2/deltat to evaluate, respectively, the Current and Trend weaning status of the newborn. Through appropriate fuzzification of these vital signs, Current and Trend weaning status can quantitatively determine the increase/decrease in the synchronized intermittent mandatory ventilation (SIMV) setting. The post-operative weaning courses of 10 newborns, 82+/-162 days old, were assessed at 2-hour intervals for 68+/-39 days. The SIMV levels, proposed by our algorithm, were matched to those levels actually applied. For 60% of the time both values coincided. For the remaining 40%, our algorithm suggested lower SIMV support than what was applied. The Area Under the Curve for integrated ventilatory support over time was 1203+/-846 for standard ventilatory strategies and 1152+/-802 for fuzzy controller. This suggests that the algorithm, approximates the actual weaning progression, and may advocate a more aggressive strategy. Moreover, the core of the fuzzy controller facilitates adaptation for body size and diversified disease patterns and sets the premises as an infant-weaning tool.

  14. Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

    Directory of Open Access Journals (Sweden)

    Chen Yu-Fan

    2006-01-01

    Full Text Available An adaptive minimum mean-square error (MMSE array receiver based on the fuzzy-logic recursive least-squares (RLS algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ( , , into a forgetting factor . For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS algorithm using the fuzzy-inference-controlled step-size . This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS and variable forgetting factor RLS (VFF-RLS algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER for multipath fading channels.

  15. A fuzzy controller for NPPs

    International Nuclear Information System (INIS)

    Schildt, G.H.

    1997-01-01

    A fuzzy controller for safety related process control is presented for applications in the field of NPPs. The size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage fuel to real-time behaviour, because program execution time is much more predictable than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principles, and quiescent current principle. (author). 3 refs, 5 figs

  16. A fuzzy controller for NPPs

    Energy Technology Data Exchange (ETDEWEB)

    Schildt, G H [Technische Univ., Vienna (Austria)

    1997-07-01

    A fuzzy controller for safety related process control is presented for applications in the field of NPPs. The size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage fuel to real-time behaviour, because program execution time is much more predictable than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principles, and quiescent current principle. (author). 3 refs, 5 figs.

  17. Design of Power Cable UAV Intelligent Patrol System Based on Adaptive Kalman Filter Fuzzy PID Control

    Directory of Open Access Journals (Sweden)

    Chen Siyu

    2017-01-01

    Full Text Available Patrol UAV has poor aerial posture stability and is largely affected by anthropic factors, which lead to some shortages such as low power cable tracking precision, captured image loss and inconvenient temperature measurement, etc. In order to solve these disadvantages, this article puts forward a power cable intelligent patrol system. The core innovation of the system is a 360° platform. This collects the position information of power cables by using far infrared sensors and carries out real-time all-direction adjustment of UAV lifting platform through the adaptive Kalman filter fuzzy PID control algorithm, so that the precise tracking of power cables is achieved. An intelligent patrol system is established to detect the faults more accurately, so that a high intelligence degree of power cable patrol system is realized.

  18. What procedure to choose while designing a fuzzy control? Towards mathematical foundations of fuzzy control

    Science.gov (United States)

    Kreinovich, Vladik YA.; Quintana, Chris; Lea, Robert

    1991-01-01

    Fuzzy control has been successfully applied in industrial systems. However, there is some caution in using it. The reason is that it is based on quite reasonable ideas, but each of these ideas can be implemented in several different ways, and depending on which of the implementations chosen different results are achieved. Some implementations lead to a high quality control, some of them not. And since there are no theoretical methods for choosing the implementation, the basic way to choose it now is experimental. But if one chooses a method that is good for several examples, there is no guarantee that it will work fine in all of them. Hence the caution. A theoretical basis for choosing the fuzzy control procedures is provided. In order to choose a procedure that transforms a fuzzy knowledge into a control, one needs, first, to choose a membership function for each of the fuzzy terms that the experts use, second, to choose operations of uncertainty values that corresponds to 'and' and 'or', and third, when a membership function for control is obtained, one must defuzzy it, that is, somehow generate a value of the control u that will be actually used. A general approach that will help to make all these choices is described: namely, it is proved that under reasonable assumptions membership functions should be linear or fractionally linear, defuzzification must be described by a centroid rule and describe all possible 'and' and 'or' operations. Thus, a theoretical explanation of the existing semi-heuristic choices is given and the basis for the further research on optimal fuzzy control is formulated.

  19. Fuzzy model-based control of a nuclear reactor

    International Nuclear Information System (INIS)

    Van Den Durpel, L.; Ruan, D.

    1994-01-01

    The fuzzy model-based control of a nuclear power reactor is an emerging research topic world-wide. SCK-CEN is dealing with this research in a preliminary stage, including two aspects, namely fuzzy control and fuzzy modelling. The aim is to combine both methodologies in contrast to conventional model-based PID control techniques, and to state advantages of including fuzzy parameters as safety and operator feedback. This paper summarizes the general scheme of this new research project

  20. Cylinder Position Servo Control Based on Fuzzy PID

    Directory of Open Access Journals (Sweden)

    Shibo Cai

    2013-01-01

    Full Text Available The arbitrary position control of cylinder has always been the hard challenge in pneumatic system. We try to develop a cylinder position servo control method by combining fuzzy PID with the theoretical model of the proportional valve-controlled cylinder system. The pressure differential equation of cylinder, pressure-flow equation of proportional valve, and moment equilibrium equation of cylinder are established. And the mathematical models of the cylinder driving system are linearized. Then fuzzy PID control algorithm is designed for the cylinder position control, including the detail analysis of fuzzy variables and domain, fuzzy logic rules, and defuzzification. The stability of the proposed fuzzy PID controller is theoretically proved according to the small gain theorem. Experiments for targets position of 250 mm, 300 mm, and 350 mm were done and the results showed that the absolute error of the position control is less than 0.25 mm. And comparative experiment between fuzzy PID and classical PID verified the advantage of the proposed algorithm.

  1. Design of fuzzy learning control systems for steam generator water level control

    International Nuclear Information System (INIS)

    Park, Gee Yong

    1996-02-01

    A fuzzy learning algorithm is developed in order to construct the useful control rules and tune the membership functions in the fuzzy logic controller used for water level control of nuclear steam generator. The fuzzy logic controllers have shown to perform better than conventional controllers for ill-defined or complex processes such as nuclear steam generator. Whereas the fuzzy logic controller does not need a detailed mathematical model of a plant to be controlled, its structure is to be made on the basis of the operator's linguistic information experienced from the plant operations. It is not an easy work and also there is no systematic way to translate the operator's linguistic information into quantitative information. When the linguistic information of operators is incomplete, tuning the parameters of fuzzy controller is to be performed for better control performance. It is the time and effort consuming procedure that controller designer has to tune the structure of fuzzy logic controller for optimal performance. And if the number of control inputs is many and the rule base is constructed in multidimensional space, it is very difficult for a controller designer to tune the fuzzy controller structure. Hence, the difficulty in putting the experimental knowledge into quantitative (or numerical) data and the difficulty in tuning the rules are the major problems in designing fuzzy logic controller. In order to overcome the problems described above, a learning algorithm by gradient descent method is included in the fuzzy control system such that the membership functions are tuned and the necessary rules are created automatically for good control performance. For stable learning in gradient descent method, the optimal range of learning coefficient not to be trapped and not to provide too slow learning speed is investigated. With the optimal range of learning coefficient, the optimal value of learning coefficient is suggested and with this value, the gradient

  2. Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration

    Science.gov (United States)

    Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza

    2003-01-01

    Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.

  3. Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model.

    Science.gov (United States)

    Li, Pengzhi; Yan, Feng; Ge, Chuan; Zhang, Mingchao

    2012-08-01

    In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.

  4. A fuzzy controller for NPPs

    International Nuclear Information System (INIS)

    Schildt, G.H.

    1996-01-01

    After an introduction into safety terms a fuzzy controller for safety related process control will be presented, especially for applications in the field of NPPs. One can show that the size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage due to real-time behaviour, because program execution time can be much more planned than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principle, and quiescent current principle

  5. Optimal reactive power and voltage control in distribution networks with distributed generators by fuzzy adaptive hybrid particle swarm optimisation method

    DEFF Research Database (Denmark)

    Chen, Shuheng; Hu, Weihao; Su, Chi

    2015-01-01

    A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid PSO (FAHPSO) is proposed. The objective is to minimize comprehensive cost, consisting of power loss and operation cost of transformers...... that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods....... algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33-node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove...

  6. Adaptive Neuro-Fuzzy Computing Technique for Determining Turbulent Flow Friction Coefficient

    Directory of Open Access Journals (Sweden)

    Mohammad Givehchi

    2013-08-01

    Full Text Available Estimation of the friction coefficient in pipes is very important in many water and wastewater engineering issues, such as distribution of velocity and shear stress, erosion, sediment transport and head loss. In analyzing these problems, knowing the friction coefficient, can obtain estimates that are more accurate. In this study in order to estimate the friction coefficient in pipes, using adaptive neuro-fuzzy inference systems (ANFIS, grid partition method was used. For training and testing of neuro-fuzzy model, the data derived from the Colebrook’s equation was used. In the neuro-fuzzy approach, pipe relative roughness and Reynolds number are considered as input variables and friction coefficient as output variable is considered. Performance of the proposed approach was evaluated by using of the data obtained from the Colebrook’s equation and based on statistical indicators such as coefficient determination (R2, root mean squared error (RMSE and mean absolute error (MAE. The results showed that the adaptive nerou-fuzzy inference system with grid partition method and gauss model as an input membership function and linear as an output function could estimate friction coefficient more accurately than other conditions. The new proposed approach in this paper has capability of application in the practical design issues and can be combined with mathematical and numerical models of sediment transfer or real-time updating of these models.

  7. Low-Speed Cooperative Car-Following Fuzzy Controller for Cybernetic Transport Systems

    OpenAIRE

    Milanés , Vicente; Marouf , Mohamed; Pérez Rastelli , Joshué; Gonzalez Bautista , David; Nashashibi , Fawzi

    2014-01-01

    International audience; — This paper describes the development of a Coop-erative Adaptive Cruise Control (CACC) for the future urban transportation system at low-speed. The control algorithm was evaluated using two Cybecars as prototype vehicles. A longitu-dinal response model for the vehicles was developed to design the CACC system. The control algorithm was implemented on a fuzzy logic-based controller that has been tuned to minimize a cost function in order to get a trade-off between a pro...

  8. Dynamic Fuzzy Logic Parameter Tuning for ACO and Its Application in the Fuzzy Logic Control of an Autonomous Mobile Robot

    Directory of Open Access Journals (Sweden)

    Oscar Castillo

    2013-01-01

    Full Text Available Ant Colony Optimization (ACO is a population-based constructive meta-heuristic that exploits a form of past performance memory inspired by the foraging behaviour of real ants. The behaviour of the ACO algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired algorithms. The present paper explores a new approach to diversity control in ACO. The central idea is to avoid or slow down full convergence through the dynamic variation of certain parameters. The performance of different variants of the ACO algorithm was observed to choose one as the basis for the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence was created. Encouraging results have been obtained on its application to the design of fuzzy controllers. In particular, the optimization of membership functions for a unicycle mobile robot trajectory control is presented with the proposed method.

  9. Design, implementation and testing of a fuzzy control scheme for laser welding

    NARCIS (Netherlands)

    Jauregui Becker, Juan Manuel; Aalderink, B.J.; Aalderink, Benno; Aarts, Ronald G.K.M.; Olde Benneker, Jeroen; Meijer, J.

    2008-01-01

    A fuzzy logic controller (FLC) scheme has been developed for laser welding. Process light emissions are measured and combined to determine the current status of the welding process. If the process is not in a desired welding state, the FLC will adapt the laser power. The FLC has been demonstrated

  10. Fuzzy Control in the Process Industry

    DEFF Research Database (Denmark)

    Jantzen, Jan; Verbruggen, Henk; Østergaard, Jens-Jørgen

    1999-01-01

    Control problems in the process industry are dominated by non-linear and time-varying behaviour, many inner loops, and much interaction between the control loops. Fuzzy controllers have in some cases nevertheless mimicked the control actions of a human operator. Simple fuzzy controllers can...... be designed starting from PID controllers, and in more complex cases these can be used in connection with model-based predictive control. For high level control and supervisory control several simple controllers can be combined in a priority hierarchy such as the one developed in the cement industry...

  11. Speed control for a two-mass drive system using integrated fuzzy estimator and hybrid fuzzy PD/PI controller

    International Nuclear Information System (INIS)

    Pai, N-S; Kuo, Y-P

    2008-01-01

    This paper presents a novel speed control scheme for a 2- mass motor drive system. The speed controller is based on the estimated state feedback compensation. The integrated fuzzy observer can give a fast and accuracy estimation of the unmeasured states. Two kinds of hybrid fuzzy proportional-derivative and proportional-integral (HF PD/PI) are proposed to cope with this speed control problem. The first is the static HF PD/PI controller and the second is the dynamic one. Simulation results show that the developed integrated fuzzy observer provide the better estimation performance than that of the Kalman filter and the proposed control schemes can effectively track the desired speed in the presence of load disturbance

  12. Neuro-fuzzy controller to navigate an unmanned vehicle.

    Science.gov (United States)

    Selma, Boumediene; Chouraqui, Samira

    2013-12-01

    A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

  13. Improvement of adaptive fuzzy control for a photovoltaic/wind/diesel generating system; Taiyoko/furyoku/diesel hatsuden system no saitekigata fuzzy seigyo no kairyo

    Energy Technology Data Exchange (ETDEWEB)

    Nagaike, H; Kenmoku, Y; Sakakibara, T [Toyohashi University of Technology, Aichi (Japan); Nakagawa, S [Maizuru National College of Technology, Kyoto (Japan); Kawamoto, T [Shizuoka University, Shizuoka (Japan).Faculty of Engineering

    1996-10-27

    The photovoltaic/wind/diesel generating system that uses a storage battery as auxiliary power has been proposed to supply power from the system to the independent area. In this system, it is important to generate no insufficient power from the viewpoint of effective energy utilization and minimize the fuel consumption of a diesel generator. Authors have proposed the adaptive fuzzy control that changes the shape of the membership function of input variables according to the parameter indicating the system state. However, a parameter was rapidly changed in the conventional method. This badly influences the control. Therefore, the way to determine the parameter that indicates the state of this system was improved. Assume that an input value is set to the average value between a certain point of time and the {Delta}t time as the method for determining a parameter. If the {Delta}t value is lower, the change in a membership function is more effective. As a result, a greater fuel reduction effect was obtained. 4 refs., 8 figs., 1 tab.

  14. Prototyping qualitative controllers for fuzzy-logic controller design

    International Nuclear Information System (INIS)

    Bakhtiari, S.; Jabedar-Maralani, P.

    1999-05-01

    Qualitative controls can be designed for linear and nonlinear models with the same computational complexity. At the same time they show the general form of the proper control. These properties can help ease the design process for quantitative controls. In this paper qualitative controls are used as prototypes for the design of linear or nonlinear, and in particular Sugeno-type fuzzy, controls. The LMS identification method is used to approximate the qualitative control with the nearest fuzzy control. The method is applied to the problem of position control in a permanent magnet synchronous motor; moreover, the performance and the robustness of the two controllers are compared

  15. Multi-stage fuzzy load frequency control using PSO

    International Nuclear Information System (INIS)

    Shayeghi, H.; Jalili, A.; Shayanfar, H.A.

    2008-01-01

    In this paper, a particle swarm optimization (PSO) based multi-stage fuzzy (PSOMSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operate under deregulation based on the bilateral policy scheme. In this strategy the control is tuned on line from the knowledge base and fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by PSO algorithm, that has a strong ability to find the most optimistic results. The motivation for using the PSO technique is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantage of PSO and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed PSO based MSF (PSOMSF) controller is tested on a three-area restructured power system under different operating conditions and contract variations. The results of the proposed PSOMSF controller are compared with genetic algorithm based multi-stage fuzzy (GAMSF) control through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes

  16. Multi-stage fuzzy load frequency control using PSO

    Energy Technology Data Exchange (ETDEWEB)

    Shayeghi, H. [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran); Jalili, A. [Islamic Azad University, Ardabil Branch, Ardabil (Iran); Shayanfar, H.A. [Center of Excellence for Power Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran)

    2008-10-15

    In this paper, a particle swarm optimization (PSO) based multi-stage fuzzy (PSOMSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operate under deregulation based on the bilateral policy scheme. In this strategy the control is tuned on line from the knowledge base and fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by PSO algorithm, that has a strong ability to find the most optimistic results. The motivation for using the PSO technique is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantage of PSO and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed PSO based MSF (PSOMSF) controller is tested on a three-area restructured power system under different operating conditions and contract variations. The results of the proposed PSOMSF controller are compared with genetic algorithm based multi-stage fuzzy (GAMSF) control through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes. (author)

  17. Obstacle avoidance for kinematically redundant robots using an adaptive fuzzy logic algorithm

    International Nuclear Information System (INIS)

    Beheshti, M.T.H.; Tehrani, A.K.

    1999-05-01

    In this paper the Adaptive Fuzzy Logic approach for solving the inverse kinematics of redundant robots in an environment with obstacles is presented. The obstacles are modeled as convex bodies. A fuzzy rule base that is updated via an adaptive law is used to solve the inverse kinematic problem. Additional rules have been introduced to take care of the obstacles avoidance problem. The proposed method has advantages such as high accuracy, simplicity of computations and generality for all redundant robots. Simulation results illustrate much better tracking performance than the dynamic base solution for a given trajectory in cartesian space, while guaranteeing a collision-free trajectory and observation of a mechanical joint limit

  18. Space-Time Joint Interference Cancellation Using Fuzzy-Inference-Based Adaptive Filtering Techniques in Frequency-Selective Multipath Channels

    Science.gov (United States)

    Hu, Chia-Chang; Lin, Hsuan-Yu; Chen, Yu-Fan; Wen, Jyh-Horng

    2006-12-01

    An adaptive minimum mean-square error (MMSE) array receiver based on the fuzzy-logic recursive least-squares (RLS) algorithm is developed for asynchronous DS-CDMA interference suppression in the presence of frequency-selective multipath fading. This receiver employs a fuzzy-logic control mechanism to perform the nonlinear mapping of the squared error and squared error variation, denoted by ([InlineEquation not available: see fulltext.],[InlineEquation not available: see fulltext.]), into a forgetting factor[InlineEquation not available: see fulltext.]. For the real-time applicability, a computationally efficient version of the proposed receiver is derived based on the least-mean-square (LMS) algorithm using the fuzzy-inference-controlled step-size[InlineEquation not available: see fulltext.]. This receiver is capable of providing both fast convergence/tracking capability as well as small steady-state misadjustment as compared with conventional LMS- and RLS-based MMSE DS-CDMA receivers. Simulations show that the fuzzy-logic LMS and RLS algorithms outperform, respectively, other variable step-size LMS (VSS-LMS) and variable forgetting factor RLS (VFF-RLS) algorithms at least 3 dB and 1.5 dB in bit-error-rate (BER) for multipath fading channels.

  19. Hierarchical fuzzy control of low-energy building systems

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Zhen; Dexter, Arthur [Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ (United Kingdom)

    2010-04-15

    A hierarchical fuzzy supervisory controller is described that is capable of optimizing the operation of a low-energy building, which uses solar energy to heat and cool its interior spaces. The highest level fuzzy rules choose the most appropriate set of lower level rules according to the weather and occupancy information; the second level fuzzy rules determine an optimal energy profile and the overall modes of operation of the heating, ventilating and air-conditioning system (HVAC); the third level fuzzy rules select the mode of operation of specific equipment, and assign schedules to the local controllers so that the optimal energy profile can be achieved in the most efficient way. Computer simulation is used to compare the hierarchical fuzzy control scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort. (author)

  20. Self-tuning fuzzy logic nuclear reactor controller

    International Nuclear Information System (INIS)

    Sharif Heger, A.; Alang-Rashid, N.K.

    1996-01-01

    We present a method for self-tuning of fuzzy logic controllers based on the estimation of the optimum value of the centroids of its output fuzzy set. The method can be implemented on-line and does not require modification of membership functions and control rules. The main features of this method are: the rules are left intact to retain the operator's expertise in the FLC rule base, and the parameters that require any adjustment are identifiable in advance and their number is kept at a minimum. Therefore, the use of this method preserves the control statements in the original form. Results of simulation and actual tests show that this tuning method improves the performance of fuzzy logic controllers in following the desired reactor power level trajectories. In addition, this method demonstrates a similar improvement for power up and power down experiments, based on both simulation and actual case studies. For these experiments, the control rules for the fuzzy logic controller were derived from control statements that expressed the relationships between error, rate of error change, and duration of direction of control rod movements

  1. Simplified Fuzzy Control for Flux-Weakening Speed Control of IPMSM Drive

    Directory of Open Access Journals (Sweden)

    M. J. Hossain

    2011-01-01

    Full Text Available This paper presents a simplified fuzzy logic-based speed control scheme of an interior permanent magnet synchronous motor (IPMSM above the base speed using a flux-weakening method. In this work, nonlinear expressions of d-axis and q-axis currents of the IPMSM have been derived and subsequently incorporated in the control algorithm for the practical purpose in order to implement fuzzy-based flux-weakening strategy to operate the motor above the base speed. The fundamentals of fuzzy logic algorithms as related to motor control applications are also illustrated. A simplified fuzzy speed controller (FLC for the IPMSM drive has been designed and incorporated in the drive system to maintain high performance standards. The efficacy of the proposed simplified FLC-based IPMSM drive is verified by simulation at various dynamic operating conditions. The simplified FLC is found to be robust and efficient. Laboratory test results of proportional integral (PI controller-based IPMSM drive have been compared with the simulated results of fuzzy controller-based flux-weakening IPMSM drive system.

  2. A Fuzzy Control Course on the TED Server

    DEFF Research Database (Denmark)

    Dotoli, Mariagrazia; Jantzen, Jan

    1999-01-01

    , an educational server that serves as a learning central for students and professionals working with fuzzy logic. Through the server, TED offers an online course on fuzzy control. The course concerns automatic control of an inverted pendulum, with a focus on rule based control by means of fuzzy logic. A ball......The Training and Education Committee (TED) is a committee under ERUDIT, a Network of Excellence for fuzzy technology and uncertainty in Europe. The main objective of TED is to improve the training and educational possibilities for the nodes of ERUDIT. Since early 1999, TED has set up the TED server...

  3. Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

    Directory of Open Access Journals (Sweden)

    Emer Bernal

    2017-01-01

    Full Text Available In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.

  4. Fuzzy adaptive integration scheme for low-cost SINS/GPS navigation system

    Science.gov (United States)

    Nourmohammadi, Hossein; Keighobadi, Jafar

    2018-01-01

    Due to weak stand-alone accuracy as well as poor run-to-run stability of micro-electro mechanical system (MEMS)-based inertial sensors, special approaches are required to integrate low-cost strap-down inertial navigation system (SINS) with global positioning system (GPS), particularly in long-term applications. This paper aims to enhance long-term performance of conventional SINS/GPS navigation systems using a fuzzy adaptive integration scheme. The main concept behind the proposed adaptive integration is the good performance of attitude-heading reference system (AHRS) in low-accelerated motions and its degradation in maneuvered or accelerated motions. Depending on vehicle maneuvers, gravity-based attitude angles can be intelligently utilized to improve orientation estimation in the SINS. Knowledge-based fuzzy inference system is developed for decision-making between the AHRS and the SINS according to vehicle maneuvering conditions. Inertial measurements are the main input data of the fuzzy system to determine the maneuvering level during the vehicle motions. Accordingly, appropriate weighting coefficients are produced to combine the SINS/GPS and the AHRS, efficiently. The assessment of the proposed integrated navigation system is conducted via real data in airborne tests.

  5. Experimental investigation of the direct torque neuro-fuzzy controller for induction motor drive

    Energy Technology Data Exchange (ETDEWEB)

    Grabowski, P.Z.; Kazmierkowski, M.P. [Warsaw Univ. of Technology (Poland)

    2000-08-01

    In this paper, the concept and implementation of a new simple Direct Torque Neuro-Fuzzy Control (DTNFC) scheme for PWM inverter-fed induction motor drive are presented. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to achieve high performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor (DSP type) TMS 320C31 based controller has been built to verify this approach. The simulation and laboratory experimental results, which illustrate the performance of the proposed scheme, are presented. Also, nomograms for controller design are given. It has been shown that the simple DTNFC is characterised by very fast torque and flux response, very low speed operation and simple tuning capability. (orig.)

  6. Modeling of Activated Sludge Process Using Sequential Adaptive Neuro-fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Mahsa Vajedi

    2014-10-01

    Full Text Available In this study, an adaptive neuro-fuzzy inference system (ANFIS has been applied to model activated sludge wastewater treatment process of Mobin petrochemical company. The correlation coefficients between the input variables and the output variable were calculated to determine the input with the highest influence on the output (the quality of the outlet flow in order to compare three neuro-fuzzy structures with different number of parameters. The predictions of the neuro-fuzzy models were compared with those of multilayer artificial neural network models with similar structure. The comparison indicated that both methods resulted in flexible, robust and effective models for the activated sludge system. Moreover, the root mean square of the error for neuro-fuzzy and neural network models were 5.14 and 6.59, respectively, which means the former is the superior method.

  7. Efficient fuzzy logic controller for magnetic levitation systems | Shu ...

    African Journals Online (AJOL)

    In this paper magnetic levitation controller using fuzzy logic is proposed. The proposed Fuzzy logic controller (FLC) is designed, and developed using triangular membership function with 7×7 rules. The system model was implemented in MATLAB/SIMULINK and the system responses to Fuzzy controller with different input ...

  8. Design of a stable fuzzy controller for an articulated vehicle.

    Science.gov (United States)

    Tanaka, K; Kosaki, T

    1997-01-01

    This paper presents a backward movement control of an articulated vehicle via a model-based fuzzy control technique. A nonlinear dynamic model of the articulated vehicle is represented by a Takagi-Sugeno fuzzy model. The concept of parallel distributed compensation is employed to design a fuzzy controller from the Takagi-Sugeno fuzzy model of the articulated vehicle. Stability of the designed fuzzy control system is guaranteed via Lyapunov approach. The stability conditions are characterized in terms of linear matrix inequalities since the stability analysis is reduced to a problem of finding a common Lyapunov function for a set of Lyapunov inequalities. Simulation results and experimental results show that the designed fuzzy controller effectively achieves the backward movement control of the articulated vehicle.

  9. A New Fuzzy Sliding Mode Controller with a Disturbance Estimator for Robust Vibration Control of a Semi-Active Vehicle Suspension System

    Directory of Open Access Journals (Sweden)

    Byung-Keun Song

    2017-10-01

    Full Text Available This paper presents a new fuzzy sliding mode controller (FSMC to improve control performances in the presence of uncertainties related to model errors and external disturbance (UAD. As a first step, an adaptive control law is designed using Lyapunov stability analysis. The control law can update control parameters of the FSMC with a disturbance estimator (DE in which the closed-loop stability and finite-time convergence of tracking error are guaranteed. A solution for estimating the compensative quantity of the impact of UAD on a control system and a set of solutions are then presented in order to avoid the singular cases of the fuzzy-based function approximation, increase convergence ability, and reduce the calculating cost. Subsequently, the effectiveness of the proposed controller is verified through the investigation of vibration control performances of a semi-active vehicle suspension system featuring a magnetorheological damper (MRD. It is shown that the proposed controller can provide better control ability of vibration control with lower consumed power compared with two existing fuzzy sliding mode controllers.

  10. Optimization of Inventories for Multiple Companies by Fuzzy Control Method

    OpenAIRE

    Kawase, Koichi; Konishi, Masami; Imai, Jun

    2008-01-01

    In this research, Fuzzy control theory is applied to the inventory control of the supply chain between multiple companies. The proposed control method deals with the amountof inventories expressing supply chain between multiple companies. Referring past demand and tardiness, inventory amounts of raw materials are determined by Fuzzy inference. The method that an appropriate inventory control becomes possible optimizing fuzzy control gain by using SA method for Fuzzy control. The variation of ...

  11. Cascade fuzzy control for gas engine driven heat pump

    International Nuclear Information System (INIS)

    Li Shuze; Zhang Wugao; Zhang Rongrong; Lv Dexu; Huang Zhen

    2005-01-01

    In addition to absorption chillers, today's gas cooling technology includes gas engine driven heat pump systems (GEHP) in a range of capacities and temperature capacities suitable for most commercial air conditioning and refrigeration applications. Much is expected from GEHPs as a product that would help satisfy the air conditioning system demand from medium and small sized buildings, restrict electric power demand peaks in summer and save energy in general. This article describes a kind of control strategy for a GEHP, a cascade fuzzy control. GEHPs have large and varying time constants and their dynamic modeling cannot be easily achieved. A cascade control strategy is effective for systems that have large time constants and disturbances, and a fuzzy control strategy is fit for a system that lacks an accurate model. This cascade fuzzy control structure brings together the best merits of fuzzy control and cascade control structures. The performance of the cascade fuzzy control is compared to that of a cascade PI (proportional and integral) control strategy, and it is shown by example that the cascade fuzzy control strategy gives a better performance, reduced reaction time and smaller overshoot temperature

  12. Implementation of Fuzzy Logic Based Temperature-Controlled Heat ...

    African Journals Online (AJOL)

    This research then compares the control performance of PID (Proportional Integral and Derivative) and Fuzzy logic controllers. Conclusions are made based on these control performances. The results show that the control performance for a Fuzzy controller is quite similar to PID controller but comparatively gives a better ...

  13. Self-learning fuzzy controllers based on temporal back propagation

    Science.gov (United States)

    Jang, Jyh-Shing R.

    1992-01-01

    This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

  14. Fuzzy Coordinated PI Controller: Application to the Real-Time Pressure Control Process

    Directory of Open Access Journals (Sweden)

    N. Kanagaraj

    2008-01-01

    Full Text Available This paper presents the real-time implementation of a fuzzy coordinated classical PI control scheme for controlling the pressure in a pilot pressure tank system. The fuzzy system has been designed to track the variation parameters in a feedback loop and tune the classical controller to achieve a better control action for load disturbances and set point changes. The error and process inputs are chosen as the inputs of fuzzy system to tune the conventional PI controller according to the process condition. This online conventional controller tuning technique will reduce the human involvement in controller tuning and increase the operating range of the conventional controller. The proposed control algorithm is experimentally implemented for the real-time pressure control of a pilot air tank system and validated using a high-speed 32-bit ARM7 embedded microcontroller board (ATMEL AT91M55800A. To demonstrate the performance of the fuzzy coordinated PI control scheme, results are compared with a classical PI and PI-type fuzzy control method. It is observed that the proposed controller structure is able to quickly track the parameter variation and perform better in load disturbances and also for set point changes.

  15. Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology

    Science.gov (United States)

    Petković, Dalibor; Shamshirband, Shahaboddin; Pavlović, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat

    2014-07-01

    The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

  16. Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS

    Directory of Open Access Journals (Sweden)

    Jani Kusanti

    2015-07-01

    Abstract             The use of Adaptive Neuro Fuzzy Inference System (ANFIS methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS. Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66%   Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE

  17. Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

    Science.gov (United States)

    Yen, John; Wang, Haojin; Daugherity, Walter C.

    1992-01-01

    Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

  18. Simple Neuron-Fuzzy Tool for Small Control Devices

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    2008-01-01

    Small control computers, running a kind of Fuzzy controller, are more and more used in many systems from household machines to large industrial systems. The purpose of this paper is firstly to describe a tool that is easy to use for implementing self learning Fuzzy systems, that can be executed...... can be described by four different kinds of membership functions. The output fuzzyfication is based on singletons. The rule base can be written in a natural language. The result of the learning is a new version of the Fuzzy system described in the FuNNy language. A simple shower control example...... is shown.  This example shows that FuNNy is able to control the shower and that the learning is able to optimize the Fuzzy system....

  19. Adaptive neuro-fuzzy optimization of wind farm project net profit

    International Nuclear Information System (INIS)

    Shamshirband, Shahaboddin; Petković, Dalibor; Ćojbašić, Žarko; Nikolić, Vlastimir; Anuar, Nor Badrul; Mohd Shuib, Nor Liyana; Mat Kiah, Miss Laiha; Akib, Shatirah

    2014-01-01

    Highlights: • Analyzing of wind farm project investment. • Net present value (NPV) maximization of the wind farm project. • Adaptive neuro-fuzzy (ANFIS) optimization of the number of wind turbines to maximize NPV. • The impact of the variation in the wind farm parameters. • Adaptive neuro fuzzy application. - Abstract: A wind power plant which consists of a group of wind turbines at a specific location is also known as wind farm. To maximize the wind farm net profit, the number of turbines installed in the wind farm should be different in depend on wind farm project investment parameters. In this paper, in order to achieve the maximal net profit of a wind farm, an intelligent optimization scheme based on the adaptive neuro-fuzzy inference system (ANFIS) is applied. As the net profit measures, net present value (NPV) and interest rate of return (IRR) are used. The NPV and IRR are two of the most important criteria for project investment estimating. The general approach in determining the accept/reject/stay in different decision for a project via NPV and IRR is to treat the cash flows as known with certainty. However, even small deviations from the predetermined values may easily invalidate the decision. In the proposed model the ANFIS estimator adjusts the number of turbines installed in the wind farm, for operating at the highest net profit point. The performance of proposed optimizer is confirmed by simulation results. Some outstanding properties of this new estimator are online implementation capability, structural simplicity and its robustness against any changes in wind farm parameters. Based on the simulation results, the effectiveness of the proposed optimization strategy is verified

  20. Hybrid Type II fuzzy system & data mining approach for surface finish

    Directory of Open Access Journals (Sweden)

    Tzu-Liang (Bill Tseng

    2015-07-01

    Full Text Available In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

  1. Fuzzy attitude control for a nanosatellite in leo orbit

    Science.gov (United States)

    Calvo, Daniel; Laverón-Simavilla, Ana; Lapuerta, Victoria; Aviles, Taisir

    Fuzzy logic controllers are flexible and simple, suitable for small satellites Attitude Determination and Control Subsystems (ADCS). In this work, a tailored fuzzy controller is designed for a nanosatellite and is compared with a traditional Proportional Integrative Derivative (PID) controller. Both control methodologies are compared within the same specific mission. The orbit height varies along the mission from injection at around 380 km down to a 200 km height orbit, and the mission requires pointing accuracy over the whole time. Due to both the requirements imposed by such a low orbit, and the limitations in the power available for the attitude control, a robust and efficient ADCS is required. For these reasons a fuzzy logic controller is implemented as the brain of the ADCS and its performance and efficiency are compared to a traditional PID. The fuzzy controller is designed in three separated controllers, each one acting on one of the Euler angles of the satellite in an orbital frame. The fuzzy memberships are constructed taking into account the mission requirements, the physical properties of the satellite and the expected performances. Both methodologies, fuzzy and PID, are fine-tuned using an automated procedure to grant maximum efficiency with fixed performances. Finally both methods are probed in different environments to test their characteristics. The simulations show that the fuzzy controller is much more efficient (up to 65% less power required) in single maneuvers, achieving similar, or even better, precision than the PID. The accuracy and efficiency improvement of the fuzzy controller increase with orbit height because the environmental disturbances decrease, approaching the ideal scenario. A brief mission description is depicted as well as the design process of both ADCS controllers. Finally the validation process and the results obtained during the simulations are described. Those results show that the fuzzy logic methodology is valid for small

  2. Design of a fuzzy logic based controller for neutron power regulation

    International Nuclear Information System (INIS)

    Velez D, D.

    2000-01-01

    This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)

  3. MRAS speed estimator with fuzzy and PI stator resistance adaptation for sensorless induction motor drives using RT-lab

    Directory of Open Access Journals (Sweden)

    S. Mohan Krishna

    2016-09-01

    Full Text Available This paper presents a real-time simulation study of Model Reference Adaptive System based rotor speed estimator with parallel stator resistance adaptation mechanism for speed sensorless induction motor drive. Both, the traditional Proportional Integral and Fuzzy logic based control mechanisms are utilised for stator resistance adaptation, while, the rotor speed is estimated parallely by means of Proportional Integral based mechanism. The estimator's response to dynamic changes in Load perturbation and doubling of the nominal value of the actual stator resistance of the motor is observed. The superiority of the fuzzy based stator resistance adaptation in the Model Reference Adaptive System estimator is proved through results validated in real-time. The purpose of employing a fairly new real-time platform is to reduce the test and prototype time. The model is initially built using Matlab/Simulink blocksets and the results are validated in real time using RT-Lab. The RT-lab blocksets are integrated into the Simulink model and then executed in real-time using the OP-4500 target developed by Opal-RT. The real-time simulation results are observed in the workstation.

  4. Differential flatness properties and adaptive control of the hypothalamic-pituitary-adrenal axis model

    Science.gov (United States)

    Rigatos, Gerasimos

    2016-12-01

    It is shown that the model of the hypothalamic-pituitary-adrenal gland axis is a differentially flat one and this permits to transform it to the so-called linear canonical form. For the new description of the system's dynamics the transformed control inputs contain unknown terms which depend on the system's parameters. To identify these terms an adaptive fuzzy approximator is used in the control loop. Thus an adaptive fuzzy control scheme is implemented in which the unknown or unmodeled system dynamics is approximated by neurofuzzy networks and next this information is used by a feedback controller that makes the state variables (CRH - corticotropin releasing hormone, adenocortocotropic hormone - ACTH, cortisol) of the hypothalamic-pituitary-adrenal gland axis model converge to the desirable levels (setpoints). This adaptive control scheme is exclusively implemented with the use of output feedback, while the state vector elements which are not directly measured are estimated with the use of a state observer that operates in the control loop. The learning rate of the adaptive fuzzy system is suitably computed from Lyapunov analysis, so as to assure that both the learning procedure for the unknown system's parameters, the dynamics of the observer and the dynamics of the control loop will remain stable. The performed Lyapunov stability analysis depends on two Riccati equations, one associated with the feedback controller and one associated with the state observer. Finally, it is proven that for the control scheme that comprises the feedback controller, the state observer and the neurofuzzy approximator, an H-infinity tracking performance can be succeeded.

  5. Fuzzy logic controller implementation for a solar air-conditioning system

    International Nuclear Information System (INIS)

    Lygouras, J.N.; Botsaris, P.N.; Vourvoulakis, J.; Kodogiannis, V.

    2007-01-01

    The implementation of a variable structure fuzzy logic controller for a solar powered air conditioning system and its advantages are investigated in this paper. Two DC motors are used to drive the generator pump and the feed pump of the solar air-conditioner. Two different control schemes for the DC motors rotational speed adjustment are implemented and tested: the first one is a pure fuzzy controller, its output being the control signal for the DC motor driver. A 7 x 7 fuzzy matrix assigns the controller output with respect to the error value and the derivative of the error. The second scheme is a two-level controller. The lower level is a conventional PID controller, and the higher level is a fuzzy controller acting over the parameters of the low level controller. Step response of the two control loops are presented as experimental results. The contribution of this design is that in the control system, the fuzzy logic is implemented through software in a common, inexpensive, 16-bit microcontroller, which does not have special abilities for fuzzy control

  6. Fuzzy logic controller implementation for a solar air-conditioning system

    Energy Technology Data Exchange (ETDEWEB)

    Lygouras, J.N.; Vourvoulakis, J. [Laboratory of Electronics, School of Electrical and Computer Engineering, Democritus University of Thrace, Vas. Sofias 12, 67100 Xanthi (Greece); Botsaris, P.N. [Laboratory of Materials, Processes and Mechanical Design, School of Production and Management Engineering, Democritus University of Thrace 67100 Xanthi (Greece); Kodogiannis, V. [Centre for Systems Analysis, School of Computer Science, University of Westminster, London, HA1 3TP (United Kingdom)

    2007-12-15

    The implementation of a variable structure fuzzy logic controller for a solar powered air conditioning system and its advantages are investigated in this paper. Two DC motors are used to drive the generator pump and the feed pump of the solar air-conditioner. Two different control schemes for the DC motors rotational speed adjustment are implemented and tested: the first one is a pure fuzzy controller, its output being the control signal for the DC motor driver. A 7 x 7 fuzzy matrix assigns the controller output with respect to the error value and the derivative of the error. The second scheme is a two-level controller. The lower level is a conventional PID controller, and the higher level is a fuzzy controller acting over the parameters of the low level controller. Step response of the two control loops are presented as experimental results. The contribution of this design is that in the control system, the fuzzy logic is implemented through software in a common, inexpensive, 16-bit microcontroller, which does not have special abilities for fuzzy control. (author)

  7. Real Time Implementation of a DC Motor Speed Control by Fuzzy Logic Controller and PI Controller Using FPGA

    Directory of Open Access Journals (Sweden)

    G. Sakthivel

    2010-10-01

    Full Text Available Fuzzy logic control has met with growing interest in many motor control applications due to its non-linearity, handling features and independence of plant modelling. The hardware implementation of fuzzy logic controller (FLC on FPGA is very important because of the increasing number of fuzzy applications requiring highly parallel and high speed fuzzy processing. Implementation of a fuzzy logic controller and conventional PI controller on an FPGA using VHDL for DC motor speed control is presented in this paper. The proposed scheme is to improve tracking performance of D.C. motor as compared to the conventional (PI control strategy .This paper describes the hardware implementation of two inputs (error and change in error, one output fuzzy logic controller based on PI controller and conventional PI controller using VHDL. Real time implementation FLC and conventional PI controller is made on Spartan-3A DSP FPGA (XC3SD1800A FPGA for the speed control of DC motor. It is observed that fuzzy logic based controllers give better responses than the conventional PI controller for the speed control of dc motor.

  8. Smart Spectrometer for Distributed Fuzzy Control

    OpenAIRE

    Benoit, Eric; Foulloy, Laurent

    2009-01-01

    Document rédigé sous FrameMaker (pas sous Latex); International audience; If the main use of colour measurement is the metrology, it is now possible to find industrial control applications which uses this information. Using colour in process control leads to specific problems where human perception has to be replaced by colour sensors. This paper relies on the fuzzy representation of colours that can be taken into account by fuzzy controllers. If smart sensors already include intelligent func...

  9. Adaptive neuro-fuzzy inference system to improve the power quality of a split shaft microturbine power generation system

    Science.gov (United States)

    Oğuz, Yüksel; Üstün, Seydi Vakkas; Yabanova, İsmail; Yumurtaci, Mehmet; Güney, İrfan

    2012-01-01

    This article presents design of adaptive neuro-fuzzy inference system (ANFIS) for the turbine speed control for purpose of improving the power quality of the power production system of a split shaft microturbine. To improve the operation performance of the microturbine power generation system (MTPGS) and to obtain the electrical output magnitudes in desired quality and value (terminal voltage, operation frequency, power drawn by consumer and production power), a controller depended on adaptive neuro-fuzzy inference system was designed. The MTPGS consists of the microturbine speed controller, a split shaft microturbine, cylindrical pole synchronous generator, excitation circuit and voltage regulator. Modeling of dynamic behavior of synchronous generator driver with a turbine and split shaft turbine was realized by using the Matlab/Simulink and SimPowerSystems in it. It is observed from the simulation results that with the microturbine speed control made with ANFIS, when the MTPGS is operated under various loading situations, the terminal voltage and frequency values of the system can be settled in desired operation values in a very short time without significant oscillation and electrical production power in desired quality can be obtained.

  10. DC motor speed control using fuzzy logic controller

    Science.gov (United States)

    Ismail, N. L.; Zakaria, K. A.; Nazar, N. S. Moh; Syaripuddin, M.; Mokhtar, A. S. N.; Thanakodi, S.

    2018-02-01

    The automatic control has played a vital role in the advance of engineering and science. Nowadays in industries, the control of direct current (DC) motor is a common practice thus the implementation of DC motor controller speed is important. The main purpose of motor speed control is to keep the rotation of the motor at the present speed and to drive a system at the demand speed. The main purpose of this project is to control speed of DC Series Wound Motor using Fuzzy Logic Controller (FLC). The expectation of this project is the Fuzzy Logic Controller will get the best performance compared to dc motor without controller in terms of settling time (Ts), rise time (Tr), peak time (Tp) and percent overshoot (%OS).

  11. A Neuro-Control Design Based on Fuzzy Reinforcement Learning

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.

    This paper describes a neuro-control fuzzy critic design procedure based on reinforcement learning. An important component of the proposed intelligent control configuration is the fuzzy credit assignment unit which acts as a critic, and through fuzzy implications provides adjustment mechanisms....... The fuzzy credit assignment unit comprises a fuzzy system with the appropriate fuzzification, knowledge base and defuzzification components. When an external reinforcement signal (a failure signal) is received, sequences of control actions are evaluated and modified by the action applier unit. The desirable...... ones instruct the neuro-control unit to adjust its weights and are simultaneously stored in the memory unit during the training phase. In response to the internal reinforcement signal (set point threshold deviation), the stored information is retrieved by the action applier unit and utilized for re...

  12. REPLACEMENT SPARE PART INVENTORY MONITORING USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

    Directory of Open Access Journals (Sweden)

    Hartono Hartono

    2016-01-01

    Full Text Available Abstract   The amount of inventory is determined on the basis of the demand. So that users can know the demand forecasts need to be done on the request. This study uses the data to implement a replacement parts on the electronic module production equipment in the telecommunications transmission systems, switching, access and power, ie by replacing the electronic module in the system is trouble  or damaged parts of a good electronic module spare parts inventory, while the faulty electronic modules shipped to the Repair Center for repaired again, so that the results of these improvements can replenish spare part  inventory. Parameters speed on improvement process of electronic module broken (repaired, in the form of an average repair time at the repair centers, in order to get back into the electronic module that is ready for used as spare parts in compliance with the safe supply inventory  warehouse.  This research using the method  of  Adaptive Neuro Fuzzy Inference System (ANFIS in developing a decision support system for inventory control of spare parts available in Warehouse Inventory taking into account several parameters supporters, namely demand, improvement and fulfillment of spare parts and repair time. This study uses a recycling input parameter repair faulty electronic module of the customer to immediately replace the module in inventory warehouse,  do improvements in the Repair Center. So the acceleration restoration factor is very influential as the input spare parts inventory supply in the warehouse and using the Adaptive Neuro-Fuzzy Inference System (ANFIS method.   Keywords: ANFIS, inventory control, replacement

  13. Development of Fuzzy Logic Controller for Quanser Bench-Top Helicopter

    Science.gov (United States)

    Jafri, M. H.; Mansor, H.; Gunawan, T. S.

    2017-11-01

    Bench-top helicopter is a laboratory scale helicopter that usually used as a testing bench of the real helicopter behavior. This helicopter is a 3 Degree of Freedom (DOF) helicopter which works by three different axes wshich are elevation, pitch and travel. Thus, fuzzy logic controller has been proposed to be implemented into Quanser bench-top helicopter because of its ability to work with non-linear system. The objective for this project is to design and apply fuzzy logic controller for Quanser bench-top helicopter. Other than that, fuzzy logic controller performance system has been simulated to analyze and verify its behavior over existing PID controller by using Matlab & Simulink software. In this research, fuzzy logic controller has been designed to control the elevation angle. After simulation has been performed, it can be seen that simulation result shows that fuzzy logic elevation control is working for 4°, 5° and 6°. These three angles produce zero steady state error and has a fast response. Other than that, performance comparisons have been performed between fuzzy logic controller and PID controller. Fuzzy logic elevation control has a better performance compared to PID controller where lower percentage overshoot and faster settling time have been achieved in 4°, 5° and 6° step response test. Both controller are have zero steady state error but fuzzy logic controller is managed to produce a better performance in term of settling time and percentage overshoot which make the proposed controller is reliable compared to the existing PID controller.

  14. Use of UPFC device controlled by fuzzy logic controllers for decoupled power flow control

    Directory of Open Access Journals (Sweden)

    Ivković Sanja

    2014-01-01

    Full Text Available This paper investigates the possibility of decoupled active and reactive power flow control in a power system using a UPFC device controlled by fuzzy logic controllers. A Brief theoretical review of the operation principles and applications of UPFC devices and design principles of the fuzzy logic controller used are given. A Matlab/Simulink model of the system with UPFC, the fuzzy controller setup, and graphs of the results are presented. Conclusions are drawn regarding the possibility of using this system for decoupled control of the power flow in power systems based on analysis of these graphs.

  15. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

    Science.gov (United States)

    Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

    2016-07-26

    This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

  16. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Chien-Hao Tseng

    2016-07-01

    Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.

  17. Coordinated signal control for arterial intersections using fuzzy logic

    Science.gov (United States)

    Kermanian, Davood; Zare, Assef; Balochian, Saeed

    2013-09-01

    Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.

  18. Application of fuzzy logic operation and control to BWRs

    International Nuclear Information System (INIS)

    Junichi Tanji; Mitsuo Kinoshita; Takaharu Fukuzaki; Yasuhiro Kobayashi

    1993-01-01

    Fuzzy logic control schemes employing linguistic decision rules for flexible operator control strategies have undergone application tests in dynamic systems. The advantages claimed for fuzzy logic control are its abilities: (a) to facilitate direct use of skillful operator know-how for automatic operation and control of the systems and (b) to provide robust multivariable control for complex plants. The authors have also studied applications of fuzzy logic control to automatic startup operations and load-following control in boiling water reactors, pursuing these same advantages

  19. Fuzzy Logic Controller Design for Intelligent Robots

    Directory of Open Access Journals (Sweden)

    Ching-Han Chen

    2017-01-01

    Full Text Available This paper presents a fuzzy logic controller by which a robot can imitate biological behaviors such as avoiding obstacles or following walls. The proposed structure is implemented by integrating multiple ultrasonic sensors into a robot to collect data from a real-world environment. The decisions that govern the robot’s behavior and autopilot navigation are driven by a field programmable gate array- (FPGA- based fuzzy logic controller. The validity of the proposed controller was demonstrated by simulating three real-world scenarios to test the bionic behavior of a custom-built robot. The results revealed satisfactorily intelligent performance of the proposed fuzzy logic controller. The controller enabled the robot to demonstrate intelligent behaviors in complex environments. Furthermore, the robot’s bionic functions satisfied its design objectives.

  20. Diagnosis Penyakit Jantung Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS

    Directory of Open Access Journals (Sweden)

    Khadijah Fahmi Hayati Holle

    2016-09-01

    Full Text Available The number of uncertain risk factor in heart disease makes experts difficult to diagnose its disease. Computer technology in the health field is mostly used. In this paper, we implement a system to diagnose heart disease. The used method is Adaptive neuro-fuzzy inference system which combine the advantage of fuzzy and neural network. The used data is UCI Cleveland data that have 13 attributes as inputs. Output system diagnosis compared with observational data for evaluation. System performance tested by calculating accuracy. Tests were also conducted on the variation of the learning rate, iteration, minimum error, and the use of membership functions. Accuracy obtained from test is 65,657% where using membership function Beta.

  1. Application of fuzzy logic control system for reactor feed-water control

    International Nuclear Information System (INIS)

    Iijima, T.; Nakajima, Y.

    1994-01-01

    The successful actual application of a fuzzy logic control system to the a nuclear Fugen nuclear power reactor is described. Fugen is a heavy-water moderated, light-water cooled reactor. The introduction of fuzzy logic control system has enabled operators to control the steam drum water level more effectively in comparison to a conventional proportional-integral (PI) control system

  2. On fuzzy control of water desalination plants

    Energy Technology Data Exchange (ETDEWEB)

    Titli, A. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Jamshidi, M. [New Mexico Univ., Albuquerque, NM (United States); Olafsson, F. [Institute of Technology, Norway (Norway)

    1995-12-31

    In this report we have chosen a sub-system of an MSF water desalination plant, the brine heater, for analysis, synthesis, and simulation. This system has been modelled and implemented on computer. A fuzzy logic controller (FLC) for the top brine temperature control loop has been designed and implemented on the computer. The performance of the proposed FLC is compared with three other conventional control strategies: PID, cascade and disturbance rejection control. One major concern on FLC`s has been the lack of stability criteria. An up to-date survey of stability of fuzzy control systems is given. We have shown stability of the proposed FLC using the Sinusoidal Input Describing Functions (SIDF) method. The potential applications of fuzzy controllers for complex and large-scale systems through hierarchy of rule sets and hybridization with conventional approaches are also investigated. (authors)

  3. On fuzzy control of water desalination plants

    Energy Technology Data Exchange (ETDEWEB)

    Titli, A [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Jamshidi, M [New Mexico Univ., Albuquerque, NM (United States); Olafsson, F [Institute of Technology, Norway (Norway)

    1996-12-31

    In this report we have chosen a sub-system of an MSF water desalination plant, the brine heater, for analysis, synthesis, and simulation. This system has been modelled and implemented on computer. A fuzzy logic controller (FLC) for the top brine temperature control loop has been designed and implemented on the computer. The performance of the proposed FLC is compared with three other conventional control strategies: PID, cascade and disturbance rejection control. One major concern on FLC`s has been the lack of stability criteria. An up to-date survey of stability of fuzzy control systems is given. We have shown stability of the proposed FLC using the Sinusoidal Input Describing Functions (SIDF) method. The potential applications of fuzzy controllers for complex and large-scale systems through hierarchy of rule sets and hybridization with conventional approaches are also investigated. (authors)

  4. Fuzzy logic controllers and chaotic natural convection loops

    International Nuclear Information System (INIS)

    Theler, German

    2007-01-01

    The study of natural circulation loops is a subject of special concern for the engineering design of advanced nuclear reactors, as natural convection provides an efficient and completely passive heat removal system. However, under certain circumstances thermal-fluid-dynamical instabilities may appear, threatening the reactor safety as a whole.On the other hand, fuzzy logic controllers provide an ideal framework to approach highly non-linear control problems. In the present work, we develop a software-based fuzzy logic controller and study its application to chaotic natural convection loops.We numerically analyse the linguistic control of the loop known as the Welander problem in such conditions that, if the controller were not present, the circulation flow would be non-periodic unstable.We also design a Taka gi-Sugeno fuzzy controller based on a fuzzy model of a natural convection loop with a toroidal geometry, in order to stabilize a Lorenz-chaotic behaviour.Finally, we show experimental results obtained in a rectangular natural circulation loop [es

  5. Fuzzy Control of Robotic Arm

    Science.gov (United States)

    Lin, Kyaw Kyaw; Soe, Aung Kyaw; Thu, Theint Theint

    2008-10-01

    This research work investigates a Self-Tuning Proportional Derivative (PD) type Fuzzy Logic Controller (STPDFLC) for a two link robot system. The proposed scheme adjusts on-line the output Scaling Factor (SF) by fuzzy rules according to the current trend of the robot. The rule base for tuning the output scaling factor is defined on the error (e) and change in error (de). The scheme is also based on the fact that the controller always tries to manipulate the process input. The rules are in the familiar if-then format. All membership functions for controller inputs (e and de) and controller output (UN) are defined on the common interval [-1,1]; whereas the membership functions for the gain updating factor (α) is defined on [0,1]. There are various methods to calculate the crisp output of the system. Center of Gravity (COG) method is used in this application due to better results it gives. Performances of the proposed STPDFLC are compared with those of their corresponding PD-type conventional Fuzzy Logic Controller (PDFLC). The proposed scheme shows a remarkably improved performance over its conventional counterpart especially under parameters variation (payload). The two-link results of analysis are simulated. These simulation results are illustrated by using MATLAB® programming.

  6. Sliding mode fuzzy control for a once-through stream generator

    International Nuclear Information System (INIS)

    Zhang Guifeng; Shi Xiaocheng; Sun Tieli; Xiong Jinkui; Zhang Hongguo

    2007-01-01

    A once-through steam generator is important equipment in nuclear power plant, so its control level is high. A Sliding Mode Fuzzy Controller inherits the robustness property of Sliding Mode Control and the interpolation property of Fuzzy Logic Control. The robustness property of variable structure system makes the control system insensitive for different burthen variety and different outside disturbance. Fuzzy control predigests the device of control system and alleviates the chattering which variable structure system causes. So the control system can be made more ideal. The paper describes the design method of Sliding Mode Fuzzy Controller without its system model for a once-through steam generator. And the simulation results show that satisfying control results can be got. (authors)

  7. Spacecraft attitude control using neuro-fuzzy approximation of the optimal controllers

    Science.gov (United States)

    Kim, Sung-Woo; Park, Sang-Young; Park, Chandeok

    2016-01-01

    In this study, a neuro-fuzzy controller (NFC) was developed for spacecraft attitude control to mitigate large computational load of the state-dependent Riccati equation (SDRE) controller. The NFC was developed by training a neuro-fuzzy network to approximate the SDRE controller. The stability of the NFC was numerically verified using a Lyapunov-based method, and the performance of the controller was analyzed in terms of approximation ability, steady-state error, cost, and execution time. The simulations and test results indicate that the developed NFC efficiently approximates the SDRE controller, with asymptotic stability in a bounded region of angular velocity encompassing the operational range of rapid-attitude maneuvers. In addition, it was shown that an approximated optimal feedback controller can be designed successfully through neuro-fuzzy approximation of the optimal open-loop controller.

  8. Fuzzy Adaptive Compensation Control of Uncertain Stochastic Nonlinear Systems With Actuator Failures and Input Hysteresis.

    Science.gov (United States)

    Wang, Jianhui; Liu, Zhi; Chen, C L Philip; Zhang, Yun

    2017-10-12

    Hysteresis exists ubiquitously in physical actuators. Besides, actuator failures/faults may also occur in practice. Both effects would deteriorate the transient tracking performance, and even trigger instability. In this paper, we consider the problem of compensating for actuator failures and input hysteresis by proposing a fuzzy control scheme for stochastic nonlinear systems. Compared with the existing research on stochastic nonlinear uncertain systems, it is found that how to guarantee a prescribed transient tracking performance when taking into account actuator failures and hysteresis simultaneously also remains to be answered. Our proposed control scheme is designed on the basis of the fuzzy logic system and backstepping techniques for this purpose. It is proven that all the signals remain bounded and the tracking error is ensured to be within a preestablished bound with the failures of hysteretic actuator. Finally, simulations are provided to illustrate the effectiveness of the obtained theoretical results.

  9. Adaptive fuzzy control of a class of nonaffine nonlinear system with input saturation based on passivity theorem.

    Science.gov (United States)

    Molavi, Ali; Jalali, Aliakbar; Ghasemi Naraghi, Mahdi

    2017-07-01

    In this paper, based on the passivity theorem, an adaptive fuzzy controller is designed for a class of unknown nonaffine nonlinear systems with arbitrary relative degree and saturation input nonlinearity to track the desired trajectory. The system equations are in normal form and its unforced dynamic may be unstable. As relative degree one is a structural obstacle in system passivation approach, in this paper, backstepping method is used to circumvent this obstacle and passivate the system step by step. Because of the existence of uncertainty and disturbance in the system, exact passivation and reference tracking cannot be tackled, so the approximate passivation or passivation with respect to a set is obtained to hold the tracking error in a neighborhood around zero. Furthermore, in order to overcome the non-smoothness of the saturation input nonlinearity, a parametric smooth nonlinear function with arbitrary approximation error is used to approximate the input saturation. Finally, the simulation results for the theoretical and practical examples are given to validate the proposed controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  10. DESCRIBING FUNCTION METHOD FOR PI-FUZZY CONTROLLED SYSTEMS STABILITY ANALYSIS

    Directory of Open Access Journals (Sweden)

    Stefan PREITL

    2004-12-01

    Full Text Available The paper proposes a global stability analysis method dedicated to fuzzy control systems containing Mamdani PI-fuzzy controllers with output integration to control SISO linear / linearized plants. The method is expressed in terms of relatively simple steps, and it is based on: the generalization of the describing function method for the considered fuzzy control systems to the MIMO case, the approximation of the describing functions by applying the least squares method. The method is applied to the stability analysis of a class of PI-fuzzy controlled servo-systems, and validated by considering a case study.

  11. CONVERGENCE OF POWERS OF CONTROLLABLE INTUITIONISTIC FUZZY MATRICES

    OpenAIRE

    Riyaz Ahmad Padder; P. Murugadas

    2016-01-01

    Convergences of powers of controllable intuitionistic fuzzy matrices have been stud¬ied. It is shown that they oscillate with period equal to 2, in general. Some equalities and sequences of inequalities about powers of controllable intuitionistic fuzzy matrices have been obtained.

  12. Fuzzy Controllers for a Gantry Crane System with Experimental Verifications

    Directory of Open Access Journals (Sweden)

    Naif B. Almutairi

    2016-01-01

    Full Text Available The control problem of gantry cranes has attracted the attention of many researchers because of the various applications of these cranes in the industry. In this paper we propose two fuzzy controllers to control the position of the cart of a gantry crane while suppressing the swing angle of the payload. Firstly, we propose a dual PD fuzzy controller where the parameters of each PD controller change as the cart moves toward its desired position, while maintaining a small swing angle of the payload. This controller uses two fuzzy subsystems. Then, we propose a fuzzy controller which is based on heuristics. The rules of this controller are obtained taking into account the knowledge of an experienced crane operator. This controller is unique in that it uses only one fuzzy system to achieve the control objective. The validity of the designed controllers is tested through extensive MATLAB simulations as well as experimental results on a laboratory gantry crane apparatus. The simulation results as well as the experimental results indicate that the proposed fuzzy controllers work well. Moreover, the simulation and the experimental results demonstrate the robustness of the proposed control schemes against output disturbances as well as against uncertainty in some of the parameters of the crane.

  13. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    Science.gov (United States)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  14. A new hybrid evolutionary algorithm based on new fuzzy adaptive PSO and NM algorithms for Distribution Feeder Reconfiguration

    International Nuclear Information System (INIS)

    Niknam, Taher; Azadfarsani, Ehsan; Jabbari, Masoud

    2012-01-01

    Highlights: ► Network reconfiguration is a very important way to save the electrical energy. ► This paper proposes a new algorithm to solve the DFR. ► The algorithm combines NFAPSO with NM. ► The proposed algorithm is tested on two distribution test feeders. - Abstract: Network reconfiguration for loss reduction in distribution system is a very important way to save the electrical energy. This paper proposes a new hybrid evolutionary algorithm to solve the Distribution Feeder Reconfiguration problem (DFR). The algorithm is based on combination of a New Fuzzy Adaptive Particle Swarm Optimization (NFAPSO) and Nelder–Mead simplex search method (NM) called NFAPSO–NM. In the proposed algorithm, a new fuzzy adaptive particle swarm optimization includes two parts. The first part is Fuzzy Adaptive Binary Particle Swarm Optimization (FABPSO) that determines the status of tie switches (open or close) and second part is Fuzzy Adaptive Discrete Particle Swarm Optimization (FADPSO) that determines the sectionalizing switch number. In other side, due to the results of binary PSO(BPSO) and discrete PSO(DPSO) algorithms highly depends on the values of their parameters such as the inertia weight and learning factors, a fuzzy system is employed to adaptively adjust the parameters during the search process. Moreover, the Nelder–Mead simplex search method is combined with the NFAPSO algorithm to improve its performance. Finally, the proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization.

  15. Introduction to type-2 fuzzy logic control theory and applications

    CERN Document Server

    Mendel, Jerry M; Tan, Woei-Wan; Melek, William W; Ying, Hao

    2014-01-01

    Written by world-class leaders in type-2 fuzzy logic control, this book offers a self-contained reference for both researchers and students. The coverage provides both background and an extensive literature survey on fuzzy logic and related type-2 fuzzy control. It also includes research questions, experiment and simulation results, and downloadable computer programs on an associated website. This key resource will prove useful to students and engineers wanting to learn type-2 fuzzy control theory and its applications.

  16. Fuzzy self-learning control for magnetic servo system

    Science.gov (United States)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  17. Robust fuzzy output feedback controller for affine nonlinear systems via T-S fuzzy bilinear model: CSTR benchmark.

    Science.gov (United States)

    Hamdy, M; Hamdan, I

    2015-07-01

    In this paper, a robust H∞ fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Contributions to fuzzy polynomial techniques for stability analysis and control

    OpenAIRE

    Pitarch Pérez, José Luis

    2014-01-01

    The present thesis employs fuzzy-polynomial control techniques in order to improve the stability analysis and control of nonlinear systems. Initially, it reviews the more extended techniques in the field of Takagi-Sugeno fuzzy systems, such as the more relevant results about polynomial and fuzzy polynomial systems. The basic framework uses fuzzy polynomial models by Taylor series and sum-of-squares techniques (semidefinite programming) in order to obtain stability guarantees...

  19. Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

    Science.gov (United States)

    Lin, Chien-Chuan; Wang, Ming-Shi

    2012-01-01

    A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. PMID:22778650

  20. A fuzzy logic pitch angle controller for power system stabilization

    Energy Technology Data Exchange (ETDEWEB)

    Jauch, Clemens; Cronin, Tom; Sorensen, Poul [Wind Energy Department, Riso National Laboratory, PO Box 49, DK-4000 Roskilde, (Denmark); Jensen, Birgitte Bak [Institute of Energy Technology, Aalborg University, Pontoppidanstraede 101, DK-9220 Aalborg East, (Denmark)

    2006-07-12

    In this article the design of a fuzzy logic pitch angle controller for a fixed speed, active-stall wind turbine, which is used for power system stabilization, is presented. The system to be controlled, which is the wind turbine and the power system to which the turbine is connected, is described. The advantages of fuzzy logic control when applied to large-signal control of active-stall wind turbines are outlined. The general steps of the design process for a fuzzy logic controller, including definition of the controller inputs, set-up of the fuzzy rules and the method of defuzzification, are described. The performance of the controller is assessed by simulation, where the wind turbine's task is to dampen power system oscillations. In the scenario simulated for this work, the wind turbine has to ride through a transient short-circuit fault and subsequently contribute to the damping of the grid frequency oscillations that are caused by the transient fault. It is concluded that the fuzzy logic controller enables the wind turbine to dampen power system oscillations. It is also concluded that, owing to the inherent non-linearities in a wind turbine and the unpredictability of the whole system, the fuzzy logic controller is very suitable for this application. (Author).

  1. T-S Fuzzy Modelling and H∞ Attitude Control for Hypersonic Gliding Vehicles

    Directory of Open Access Journals (Sweden)

    Weidong Zhang

    2017-01-01

    Full Text Available This paper addresses the T-S fuzzy modelling and H∞ attitude control in three channels for hypersonic gliding vehicles (HGVs. First, the control-oriented affine nonlinear model has been established which is transformed from the reentry dynamics. Then, based on Taylor’s expansion approach and the fuzzy linearization approach, the homogeneous T-S local modelling technique for HGVs is proposed. Given the approximation accuracy and controller design complexity, appropriate fuzzy premise variables and operating points of interest are selected to construct the T-S homogeneous submodels. With so-called fuzzy blending, the original plant is transformed into the overall T-S fuzzy model with disturbance. By utilizing Lyapunov functional approach, a state feedback fuzzy controller has been designed based on relaxed linear matrix inequality (LMI conditions to stable the original plants with a prescribed H∞ performance of disturbance. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed H∞ T-S fuzzy controller for the original attitude dynamics; the superiority of the designed T-S fuzzy controller compared with other local controllers based on the constructed fuzzy model is shown as well.

  2. Enhancing transparent fuzzy controllers through temporal concepts : an application to computer games

    NARCIS (Netherlands)

    Acampora, G.; Loia, V.; Vitiello, A.

    2010-01-01

    In the last years, FML (Fuzzy Markup Language) is emerging as one of the most efficient and useful language to define a fuzzy control thanks to its capability of modeling Fuzzy Logic Controllers in a human-readable and hardware independent way, i.e. the so-called Transparent Fuzzy Controllers

  3. Experimental analysis of fuzzy controlled energy efficient demand controlled ventilation economizer cycle variable air volume air conditioning system

    Directory of Open Access Journals (Sweden)

    Rajagopalan Parameshwaran

    2008-01-01

    Full Text Available In the quest for energy conservative building design, there is now a great opportunity for a flexible and sophisticated air conditioning system capable of addressing better thermal comfort, indoor air quality, and energy efficiency, that are strongly desired. The variable refrigerant volume air conditioning system provides considerable energy savings, cost effectiveness and reduced space requirements. Applications of intelligent control like fuzzy logic controller, especially adapted to variable air volume air conditioning systems, have drawn more interest in recent years than classical control systems. An experimental analysis was performed to investigate the inherent operational characteristics of the combined variable refrigerant volume and variable air volume air conditioning systems under fixed ventilation, demand controlled ventilation, and combined demand controlled ventilation and economizer cycle techniques for two seasonal conditions. The test results of the variable refrigerant volume and variable air volume air conditioning system for each techniques are presented. The test results infer that the system controlled by fuzzy logic methodology and operated under the CO2 based mechanical ventilation scheme, effectively yields 37% and 56% per day of average energy-saving in summer and winter conditions, respectively. Based on the experimental results, the fuzzy based combined system can be considered to be an alternative energy efficient air conditioning scheme, having significant energy-saving potential compared to the conventional constant air volume air conditioning system.

  4. Type-2 fuzzy logic uncertain systems’ modeling and control

    CERN Document Server

    Antão, Rómulo

    2017-01-01

    This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.

  5. Approximation-Based Discrete-Time Adaptive Position Tracking Control for Interior Permanent Magnet Synchronous Motors.

    Science.gov (United States)

    Yu, Jinpeng; Shi, Peng; Yu, Haisheng; Chen, Bing; Lin, Chong

    2015-07-01

    This paper considers the problem of discrete-time adaptive position tracking control for a interior permanent magnet synchronous motor (IPMSM) based on fuzzy-approximation. Fuzzy logic systems are used to approximate the nonlinearities of the discrete-time IPMSM drive system which is derived by direct discretization using Euler method, and a discrete-time fuzzy position tracking controller is designed via backstepping approach. In contrast to existing results, the advantage of the scheme is that the number of the adjustable parameters is reduced to two only and the problem of coupling nonlinearity can be overcome. It is shown that the proposed discrete-time fuzzy controller can guarantee the tracking error converges to a small neighborhood of the origin and all the signals are bounded. Simulation results illustrate the effectiveness and the potentials of the theoretic results obtained.

  6. Neural-Fuzzy Digital Strategy of Continuous-Time Nonlinear Systems Using Adaptive Prediction and Random-Local-Optimization Design

    Directory of Open Access Journals (Sweden)

    Zhi-Ren Tsai

    2013-01-01

    Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.

  7. Fuzzy logic control of water level in advanced boiling water reactor

    International Nuclear Information System (INIS)

    Lin, Chaung; Lee, Chi-Szu; Raghavan, R.; Fahrner, D.M.

    1995-01-01

    The feedwater control system in the Advanced Boiling Water Reactor (ABWR) is more challenging to design compared to other control systems in the plant, due to the possible change in level from void collapses and swells during transient events. A basic fuzzy logic controller is developed using a simplified ABWR mathematical model to demonstrate and compare the performance of this controller with a simplified conventional controller. To reduce the design effort, methods are developed to automatically tune the scaling factors and control rules. As a first step in developing the fuzzy controller, a fuzzy controller with a limited number of rules is developed to respond to normal plant transients such as setpoint changes of plant parameters and load demand changes. Various simulations for setpoint and load demand changes of plant performances were conducted to evaluate the modeled fuzzy logic design against the simplified ABWR model control system. The simulation results show that the performance of the fuzzy logic controller is comparable to that of the Proportional-Integral (PI) controller, However, the fuzzy logic controller produced shorter settling time for step setpoint changes compared to the simplified conventional controller

  8. Localized and Energy-Efficient Topology Control in Wireless Sensor Networks Using Fuzzy-Logic Control Approaches

    Directory of Open Access Journals (Sweden)

    Yuanjiang Huang

    2014-01-01

    Full Text Available The sensor nodes in the Wireless Sensor Networks (WSNs are prone to failures due to many reasons, for example, running out of battery or harsh environment deployment; therefore, the WSNs are expected to be able to maintain network connectivity and tolerate certain amount of node failures. By applying fuzzy-logic approach to control the network topology, this paper aims at improving the network connectivity and fault-tolerant capability in response to node failures, while taking into account that the control approach has to be localized and energy efficient. Two fuzzy controllers are proposed in this paper: one is Learning-based Fuzzy-logic Topology Control (LFTC, of which the fuzzy controller is learnt from a training data set; another one is Rules-based Fuzzy-logic Topology Control (RFTC, of which the fuzzy controller is obtained through designing if-then rules and membership functions. Both LFTC and RFTC do not rely on location information, and they are localized. Comparing them with other three representative algorithms (LTRT, List-based, and NONE through extensive simulations, our two proposed fuzzy controllers have been proved to be very energy efficient to achieve desired node degree and improve the network connectivity when sensor nodes run out of battery or are subject to random attacks.

  9. Multivariable fuzzy modelling and command: application to the thermic engine control; Modelisation et commande multivariables floues: application a la commande d`un moteur thermique

    Energy Technology Data Exchange (ETDEWEB)

    Bortolet, P.

    1998-12-11

    During these last two decades, the growing awareness of the contribution of the automobile to the degradation of the environment has forces different figures from the transportation world to put automobiles under more and more severe controls. Fuzzy logic is a technique which allows for the taking into account of experts knowledge; the most recent research work has moreover shown interest in associating fuzzy logic with algorithmic control techniques (adaptive control, robust control...). Our research work can be broken down into three distinct parts: a theoretical approach concerning the methods of fuzzy modeling permitting one to achieve models of the type Takagi-Sugeno and to use them in the synthesis of controls; the work of physical modeling of a four-stroke direct injection gas motor in collaboration with the development teams from Siemens Automotive SA; the simulated application of fuzzy modeling techniques and of fuzzy control developed on a theoretical level to a four-stroke direct injection gas motor. (author) 105 refs.

  10. Fuzzy controller of speed-power of a synchronous micro generator; Controlador difuso de velocidad-potencia de un microgenerador sincrono

    Energy Technology Data Exchange (ETDEWEB)

    Martinez Alvarado, Raziel

    2002-11-01

    This thesis shows the design and implementation of a speed-power fuzzy-logic controller. The controller implementation was carried out on the Schrage motor-synchronous generator set. The synchronous alternator is rated 7 kVA, 220 V, 1800 rpm, 60 Hz. Two PI like fuzzy-logic controllers were developed with 9 and 25 rules. The controllers use the speed or power error and its integral as input variables and as an output the control signal from the brush-positioner of the Schrage motor. At the controller design stage, the anfis (adaptive-network-based fuzzy inference system) learning and structure procedure was used for tuning up parameters of the membership functions used on the designed fuzzy controllers. These controllers are first-order Sugeno-type. The designed controllers were tested on the motor-generator set under loaded and no-loaded conditions. It was found that PI-9 rules fuzzy-logic controller had better performance on both operating conditions. [Spanish] En este trabajo de tesis se presenta el diseno e implementacion de un controlador difuso de velocidad-potencia, para un grupo motor Schrage-generador sincrono de 7 kVA, 220 V, 1800 rpm, 60 Hz. Se implementaron controladores difusos del tipo PI de 9 y 25 reglas. Estos controladores utilizan como variables de entrada el error y la integral del error, de velocidad o potencia segun corresponda, y como variable de salida la senal de control del posicionador de las escobillas del motor Schrage. En la etapa de diseno de los controladores, se utilizo la estructura y el procedimiento de aprendizaje anfis (Sistema de Inferencia Difuso Basado en Redes Adaptables, por sus siglas en ingles) para sintonizar los parametros de las funciones de membresia de los controladores difusos, los cuales son del tipo Sugeno de primer orden. Con la finalidad de validar los controladores disenados, se realizaron pruebas experimentales al grupo motor-generador en condiciones de vacio y carga. Se encontro que el controlador difuso tipo

  11. Real Time Implementation of Incremental Fuzzy Logic Controller for Gas Pipeline Corrosion Control

    Directory of Open Access Journals (Sweden)

    Gopalakrishnan Jayapalan

    2014-01-01

    Full Text Available A robust virtual instrumentation based fuzzy incremental corrosion controller is presented to protect metallic gas pipelines. Controller output depends on error and change in error of the controlled variable. For corrosion control purpose pipe to soil potential is considered as process variable. The proposed fuzzy incremental controller is designed using a very simple control rule base and the most natural and unbiased membership functions. The proposed scheme is tested for a wide range of pipe to soil potential control. Performance comparison between the conventional proportional integral type and proposed fuzzy incremental controller is made in terms of several performance criteria such as peak overshoot, settling time, and rise time. Result shows that the proposed controller outperforms its conventional counterpart in each case. Designed controller can be taken in automode without waiting for initial polarization to stabilize. Initial startup curve of proportional integral controller and fuzzy incremental controller is reported. This controller can be used to protect any metallic structures such as pipelines, tanks, concrete structures, ship, and offshore structures.

  12. Application of fuzzy control in cooling systems save energy design

    Energy Technology Data Exchange (ETDEWEB)

    Chen, M.L.; Liang, H.Y. [Chienkuo Technology Univ., Changhua, Taiwan (China). Dept. of Electrical Engineering

    2005-07-01

    A fuzzy logic programmable logic controller (PLC) was used to control the cooling systems of frigorific equipment. Frigorific equipment is used to move unwanted heat outside of building in order to control indoor temperatures. The aim of the fuzzy logic PLC was to improve the energy efficiency of the cooling system. Control of the cooling pump and cooling tower in the system was based on the water temperature of the condenser during frigorific system operation. A human computer design for the cooling system control was used to set speeds and to automate and adjust the motor according to the fuzzy logic controller. It was concluded that if fuzzy logic controllers are used with all components of frigorific equipment, energy efficiency will be significantly increased. 5 refs., 3 tabs., 9 figs.

  13. Adaptive inferential sensors based on evolving fuzzy models.

    Science.gov (United States)

    Angelov, Plamen; Kordon, Arthur

    2010-04-01

    A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the

  14. Hybrid fuzzy logic control of laser surface heat treatments

    International Nuclear Information System (INIS)

    Perez, Jose Antonio; Ocana, Jose Luis; Molpeceres, Carlos

    2007-01-01

    This paper presents an advanced hybrid fuzzy logic control system for laser surface heat treatments, which allows to increase significantly the uniformity and final quality of the obtained product, reducing the rejection rate and increasing the productivity and efficiency of the treatment. Basically, the proposed hybrid control structure combines a fuzzy logic controller, with a pure integral action, both fully decoupled, improving the performances of the process with a reasonable design cost, since the system nonlinearities are fully compensated by the fuzzy component of the controller, while the integral action contributes to eliminate the steady-state error

  15. The research on visual industrial robot which adopts fuzzy PID control algorithm

    Science.gov (United States)

    Feng, Yifei; Lu, Guoping; Yue, Lulin; Jiang, Weifeng; Zhang, Ye

    2017-03-01

    The control system of six degrees of freedom visual industrial robot based on the control mode of multi-axis motion control cards and PC was researched. For the variable, non-linear characteristics of industrial robot`s servo system, adaptive fuzzy PID controller was adopted. It achieved better control effort. In the vision system, a CCD camera was used to acquire signals and send them to video processing card. After processing, PC controls the six joints` motion by motion control cards. By experiment, manipulator can operate with machine tool and vision system to realize the function of grasp, process and verify. It has influence on the manufacturing of the industrial robot.

  16. Advances in type-2 fuzzy sets and systems theory and applications

    CERN Document Server

    Mendel, Jerry; Tahayori, Hooman

    2013-01-01

    This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-2 fuzzy logic controllers, relations between conceptual spaces and type-2 fuzzy sets, type-2 fuzzy logic systems versus perceptual computers; modeling human perception of real world concepts with type-2 fuzzy sets, different methods for generating membership functions of interval and general type-2 fuzzy sets, and applications of interval type-2 fuzzy sets to control, machine tooling, image processing and diet.  The applications demonstrate the appropriateness of using type-2 fuzzy sets and systems in real world problems that are characterized by different degrees of uncertainty.

  17. A robust adaptive load frequency control for micro-grids.

    Science.gov (United States)

    Khooban, Mohammad-Hassan; Niknam, Taher; Blaabjerg, Frede; Davari, Pooya; Dragicevic, Tomislav

    2016-11-01

    The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α-plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Power control of SAFE reactor using fuzzy logic

    International Nuclear Information System (INIS)

    Irvine, Claude

    2002-01-01

    Controlling the 100 kW SAFE (Safe Affordable Fission Engine) reactor consists of design and implementation of a fuzzy logic process control system to regulate dynamic variables related to nuclear system power. The first phase of development concentrates primarily on system power startup and regulation, maintaining core temperature equilibrium, and power profile matching. This paper discusses the experimental work performed in those areas. Nuclear core power from the fuel elements is simulated using resistive heating elements while heat rejection is processed by a series of heat pipes. Both axial and radial nuclear power distributions are determined from neuronic modeling codes. The axial temperature profile of the simulated core is matched to the nuclear power profile by varying the resistance of the heating elements. The SAFE model establishes radial temperature profile equivalence by establishing 32 control zones as the nodal coordinates. Control features also allow for slow warm up, since complete shutoff can occur in the heat pipes if heat-source temperatures drop/rise below a certain minimum value, depending on the specific fluid and gas combination in the heat pipe. The entire system is expected to be self-adaptive, i.e., capable of responding to long-range changes in the space environment. Particular attention in the development of the fuzzy logic algorithm shall ensure that the system process remains at set point, virtually eliminating overshoot on start-up and during in-process disturbances. The controller design will withstand harsh environments and applications where it might come in contact with water, corrosive chemicals, radiation fields, etc

  19. Application of Fuzzy Control in a Photovoltaic Grid-Connected Inverter

    Directory of Open Access Journals (Sweden)

    Zhaohong Zheng

    2018-01-01

    Full Text Available To realize the maximum power output of a grid-connected inverter, the MPPT (maximum power point tracking control method is needed. The perturbation and observation (P&O method can cause the inverter operating point to oscillate near the maximum power. In this paper, the fuzzy control P&O method is proposed, and the fuzzy control algorithm is applied to the disturbance observation method. The simulation results of the P&O method with fuzzy control and the traditional P&O method prove that not only can the new method reduce the power loss caused by inverter oscillation during maximum power point tracking, but also it has the advantage of speed. Inductive loads in the post-grid-connected stage cause grid-connected current distortion. A fuzzy control algorithm is added to the traditional deadbeat grid-connected control method to improve the quality of the system’s grid-connected operation. The fuzzy deadbeat control method is verified by experiments, and the harmonic current of the grid-connected current is less than 3%.

  20. Monitoring of surge tanks in hydroelectric power plants using fuzzy control; Ueberwachung von Wasserschloessern in Wasserkraftwerken mit Fuzzy-Control

    Energy Technology Data Exchange (ETDEWEB)

    Lin, J.C.

    2000-07-01

    Surge tanks are used to reduce pressure variations caused by fluid transients in high-head hydroelectric power plants. Occasionally load increases have to be limited to prevent the surge tank from draining due to excessive demands of flow. A control concept based on fuzzy logic was developed for governing the load changes of hydroelectric power plants. In order to achieve an optimal control behaviour and simultaneously to avoid the draining of surge tanks, the speed of load increases is automatically adjusted by a fuzzy conclusion depending on the height and the gradient of the water level in the surge tank, the reservoir level and the sum of load increases. The hydroelectric power plant Achensee of Tiroler Wasserkraftwerke AG in Austria is taken as an example to demonstrate the characteristics of the control concept. In comparison with a conventional control concept, the operation of load increases using the fuzzy concept proves to be more flexible and unrestricted. (orig.) [German] Ein Wasserschloss dient zur Verminderung von Druckschwankungen im Wasserfuehrungssystem von Hochdruckanlagen. Gelegentlich muss man die Lastaufnahme so beschraenken, dass das Wasserschloss nicht durch uebermaessige Wasserentnahme leerlaeuft. Fuer die Leistungsregelung eines Wasserkraftwerks wurde ein Konzept entwickelt, das auf der Fuzzy-Control in Verbindung mit der klassischen Regelung beruht. Um ein optimales Regelverhalten zu erhalten und gleichzeitig das Leerlaufen des Wasserschlosses zu vermeiden, wird die Geschwindigkeit der Lastaufnahme in Abhaengigkeit von der Hoehenkote und dem Gradienten des Wasserschlosspegels, dem Pegel des Oberwassers und der Groesse der geforderten Lasterhoehung automatisch eingestellt. Die Untersuchung erfolgt am Beispiel des Achenseekraftwerkes der Tiroler Wasserkraftwerke AG, Oesterreich. Im Vergleich mit einer konventionellen Regelung ergibt sich mit dem Fuzzy-Konzept eine flexiblere und freizuegigere Lastaufnahme. (orig.)

  1. An automatic tuning method of a fuzzy logic controller for nuclear reactors

    International Nuclear Information System (INIS)

    Ramaswamy, P.; Lee, K.Y.; Edwards, R.M.

    1993-01-01

    The design and evaluation by simulation of an automatically tuned fuzzy logic controller is presented. Typically, fuzzy logic controllers are designed based on an expert's knowledge of the process. However, this approach has its limitations in the fact that the controller is hard to optimize or tune to get the desired control action. A method to automate the tuning process using a simplified Kalman filter approach is presented for the fuzzy logic controller to track a suitable reference trajectory. Here, for purposes of illustration an optimal controller's response is used as a reference trajectory to determine automatically the rules for the fuzzy logic controller. To demonstrate the robustness of this design approach, a nonlinear six-delayed neutron group plant is controlled using a fuzzy logic controller that utilizes estimated reactor temperatures from a one-delayed neutron group observer. The fuzzy logic controller displayed good stability and performance robustness characteristics for a wide range of operation

  2. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

    2015-02-01

    This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

  3. Fuzzy logic applied to the control of the energy consumption in intelligent buildings; Logica fuzzy aplicada ao controle do consumo de energia eletrica em edificios inteligentes

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Herbert R. do N.

    1998-02-01

    This work shows a study on the using of fuzzy control algorithms for the energy optimization of a standard building. The simulation of this type of control was performed using a central conditioned air model and the fuzzy control architecture already used in various control projects. This situation allowed a comparative study among the the control algorithms normally used in conditioned air installations, and the control performed through the building automation system, using an algorithm based on fuzzy logic.

  4. Optimization of type-2 fuzzy controllers using the bee colony algorithm

    CERN Document Server

    Amador, Leticia

    2017-01-01

    This book focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm and also considering the fuzzy control area. The main idea is that this areas together can to solve various control problems and to find better results. In this book we test the proposed method using two benchmark problems; the problem for filling a water tank and the problem for controlling the trajectory in an autonomous mobile robot. When Interval Type-2 Fuzzy Logic System is implemented to model the behavior of systems, the results show a better stabilization, because the analysis of uncertainty is better. For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.

  5. Fuzzy Logic Temperature Control System For The Induction Furnace

    Directory of Open Access Journals (Sweden)

    Lei Lei Hnin

    2015-08-01

    Full Text Available This research paper describes the fuzzy logic temperature control system of the induction furnace. Temperature requirement of the heating system varies during the heating process. In the conventional control schemes the switching losses increase with the change in the load. A closed loop control is required to have a smooth control on the system. In this system pulse width modulation based power control scheme for the induction heating system is developed using the fuzzy logic controller. The induction furnace requires a good voltage regulation to have efficient response. The controller controls the temperature depending upon weight of meat water and time. This control system is implemented in hardware system using microcontroller. Here the fuzzy logic controller is designed and simulated in MATLAB to get the desire condition.

  6. Designing an Energy Storage System Fuzzy PID Controller for Microgrid Islanded Operation

    Directory of Open Access Journals (Sweden)

    Jin-Hong Jeon

    2011-09-01

    Full Text Available Recently, interest in microgrids, which are composed of distributed generation (DG, distributed storage (DS, and loads, has been growing as a potentially effective clean energy system to mitigate against climate change. The microgrid is operated in the grid-connected mode and the islanded mode according to the conditions of the upstream power grid. The role of the energy storage system (ESS is especially important to maintain constant the frequency and voltage of an islanded microgrid. For this reason, various approaches for ESS control have been put forth. In this paper, a fuzzy PID controller is proposed to improve the frequency control performance of the ESS. This fuzzy PID controller consists of a fuzzy logic controller and a conventional PI controller, connected in series. The fuzzy logic controller has two input signals, and then the output signal of the fuzzy logic controller is the input signal of the conventional PI controller. For comparison of control performance, gains of each PI controller and fuzzy PID controller are tuned by the particle swam optimization (PSO algorithm. In the simulation study, the control performance of the fuzzy PID was also tested under various operating conditions using the PSCAD/EMTDC simulation platform.

  7. Fuzzy multivariable control of domestic heat pumps

    International Nuclear Information System (INIS)

    Underwood, C.P.

    2015-01-01

    Poor control has been identified as one of the reasons why recent field trials of domestic heat pumps in the UK have produced disappointing results. Most of the technology in use today uses a thermostatically-controlled fixed speed compressor with a mechanical expansion device. This article investigates improved control of these heat pumps through the design and evaluation of a new multivariable fuzzy logic control system utilising a variable speed compressor drive with capacity control linked through to evaporator superheat control. A new dynamic thermal model of a domestic heat pump validated using experimental data forms the basis of the work. The proposed control system is evaluated using median and extreme daily heating demand profiles for a typical UK house compared with a basic thermostatically-controlled alternative. Results show good tracking of the heating temperature and superheat control variables, reduced cycling and an improvement in performance averaging 20%. - Highlights: • A new dynamic model of a domestic heat pump is developed and validated. • A new multivariable fuzzy logic heat pump control system is developed/reported. • The fuzzy controller regulates both plant capacity and evaporator superheat degree. • Thermal buffer storage is also considered as well as compressor cycling. • The new controller shows good variable tracking and a reduction in energy of 20%.

  8. Fuzzy-Based Adaptive Hybrid Burst Assembly Technique for Optical Burst Switched Networks

    Directory of Open Access Journals (Sweden)

    Abubakar Muhammad Umaru

    2014-01-01

    Full Text Available The optical burst switching (OBS paradigm is perceived as an intermediate switching technology for future all-optical networks. Burst assembly that is the first process in OBS is the focus of this paper. In this paper, an intelligent hybrid burst assembly algorithm that is based on fuzzy logic is proposed. The new algorithm is evaluated against the traditional hybrid burst assembly algorithm and the fuzzy adaptive threshold (FAT burst assembly algorithm via simulation. Simulation results show that the proposed algorithm outperforms the hybrid and the FAT algorithms in terms of burst end-to-end delay, packet end-to-end delay, and packet loss ratio.

  9. Control of a Quadrotor Using a Smart Self-Tuning Fuzzy PID Controller

    Directory of Open Access Journals (Sweden)

    Deepak Gautam

    2013-11-01

    Full Text Available This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra's algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.

  10. Artifact reduction of compressed images and video combining adaptive fuzzy filtering and directional anisotropic diffusion

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren; Korhonen, Jari

    2011-01-01

    and ringing artifacts, we have applied directional anisotropic diffusion. Besides that, the selection of the adaptive threshold parameter for the diffusion coefficient has also improved the performance of the algorithm. Experimental results on JPEG compressed images as well as MJPEG and H.264 compressed......Fuzzy filtering is one of the recently developed methods for reducing distortion in compressed images and video. In this paper, we combine the powerful anisotropic diffusion equations with fuzzy filtering in order to reduce the impact of artifacts. Based on the directional nature of the blocking...... videos show improvement in artifact reduction of the proposed algorithm over other directional and spatial fuzzy filters....

  11. Fuzzy model-based servo and model following control for nonlinear systems.

    Science.gov (United States)

    Ohtake, Hiroshi; Tanaka, Kazuo; Wang, Hua O

    2009-12-01

    This correspondence presents servo and nonlinear model following controls for a class of nonlinear systems using the Takagi-Sugeno fuzzy model-based control approach. First, the construction method of the augmented fuzzy system for continuous-time nonlinear systems is proposed by differentiating the original nonlinear system. Second, the dynamic fuzzy servo controller and the dynamic fuzzy model following controller, which can make outputs of the nonlinear system converge to target points and to outputs of the reference system, respectively, are introduced. Finally, the servo and model following controller design conditions are given in terms of linear matrix inequalities. Design examples illustrate the utility of this approach.

  12. H∞ Control of Polynomial Fuzzy Systems: A Sum of Squares Approach

    Directory of Open Access Journals (Sweden)

    Bomo W. Sanjaya

    2014-07-01

    Full Text Available This paper proposes the control design ofa nonlinear polynomial fuzzy system with H∞ performance objective using a sum of squares (SOS approach. Fuzzy model and controller are represented by a polynomial fuzzy model and controller. The design condition is obtained by using polynomial Lyapunov functions that not only guarantee stability but also satisfy the H∞ performance objective. The design condition is represented in terms of an SOS that can be numerically solved via the SOSTOOLS. A simulation study is presented to show the effectiveness of the SOS-based H∞ control designfor nonlinear polynomial fuzzy systems.

  13. Electric Drive Control with Rotor Resistance and Rotor Speed Observers Based on Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    C. Ben Regaya

    2014-01-01

    Full Text Available Many scientific researchers have proposed the control of the induction motor without speed sensor. These methods have the disadvantage that the variation of the rotor resistance causes an error of estimating the motor speed. Thus, simultaneous estimation of the rotor resistance and the motor speed is required. In this paper, a scheme for estimating simultaneously the rotor resistance and the rotor speed of an induction motor using fuzzy logic has been developed. We present a method which is based on two adaptive observers using fuzzy logic without affecting each other and a simple algorithm in order to facilitate the determination of the optimal values of the controller gains. The control algorithm is proved by the simulation tests. The results analysis shows the characteristic robustness of the two observers of the proposed method even in the case of variation of the rotor resistance.

  14. Optimization Settings in the Fuzzy Combined Mamdani PID Controller

    Science.gov (United States)

    Kudinov, Y. I.; Pashchenko, F. F.; Pashchenko, A. F.; Kelina, A. Y.; Kolesnikov, V. A.

    2017-11-01

    In the present work the actual problem of determining the optimal settings of fuzzy parallel proportional-integral-derivative (PID) controller is considered to control nonlinear plants that is not always possible to perform with classical linear PID controllers. In contrast to the linear fuzzy PID controllers there are no analytical methods of settings calculation. In this paper, we develop a numerical optimization approach to determining the coefficients of a fuzzy PID controller. Decomposition method of optimization is proposed, the essence of which was as follows. All homogeneous coefficients were distributed to the relevant groups, for example, three error coefficients, the three coefficients of the changes of errors and the three coefficients of the outputs P, I and D components. Consistently in each of such groups the search algorithm was selected that has determined the coefficients under which we receive the schedule of the transition process satisfying all the applicable constraints. Thus, with the help of Matlab and Simulink in a reasonable time were found the factors of a fuzzy PID controller, which meet the accepted limitations on the transition process.

  15. Fuzzy Control of a Lead Acid Battery Charger

    Directory of Open Access Journals (Sweden)

    A. DAOUD

    2005-03-01

    Full Text Available In this paper, an alternative battery charging control technique based on fuzzy logic for photovoltaic (PV applications is presented. A PV module is connected to a buck type DC/DC power converter and a microcontroller based unit is used to control the lead acid battery charging voltage. The fuzzy control is used due to the simplicity of implementation, robustness and independence from the complex mathematical representation of the battery. The usefulness of this control method is confirmed by experiments.

  16. A fuzzy-logic antiswing controller for three-dimensional overhead cranes.

    Science.gov (United States)

    Cho, Sung-Kun; Lee, Ho-Hoon

    2002-04-01

    In this paper, a new fuzzy antiswing control scheme is proposed for a three-dimensional overhead crane. The proposed control consists of a position servo control and a fuzzy-logic control. The position servo control is used to control crane position and rope length, and the fuzzy-logic control is used to suppress load swing. The proposed control guarantees not only prompt suppression of load swing but also accurate control of crane position and rope length for simultaneous travel, traverse, and hoisting motions of the crane. Furthermore, the proposed control provides practical gain tuning criteria for easy application. The effectiveness of the proposed control is shown by experiments with a three-dimensional prototype overhead crane.

  17. Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

    Science.gov (United States)

    Yi, J.; Choi, C.

    2014-12-01

    Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

  18. Synchronization and secure communication of chaotic systems via robust adaptive high-gain fuzzy observer

    International Nuclear Information System (INIS)

    Hyun, Chang-Ho; Park, Chang-Woo; Kim, Jae-Hun; Park, Mignon

    2009-01-01

    This paper proposes an alternative robust adaptive high-gain fuzzy observer design scheme and its application to synchronization and secure communication of chaotic systems. It is assumed that their states are immeasurable and their parameters are unknown. The structure of the proposed observer is represented by Takagi-Sugeno fuzzy model and has the integrator of the estimation error. It improves the performance of high-gain observer and makes the proposed observer robust against noisy measurements, uncertainties and parameter perturbations as well. Using Lyapunov stability theory, an adaptive law is derived to estimate the unknown parameters and the stability of the proposed observer is analyzed. Some simulation result of synchronization and secure communication of chaotic systems is given to present the validity of theoretical derivations and the performance of the proposed observer as an application.

  19. A Fuzzy-PI control to extract an optimal power from wind turbine

    International Nuclear Information System (INIS)

    Aissaoui, Abdel Ghani; Tahour, Ahmed; Essounbouli, Najib; Nollet, Frédéric; Abid, Mohamed; Chergui, Moulay Idriss

    2013-01-01

    Highlights: ► We model the wind energy conversion system (WECS) based on the PMSG. ► We present the vector control of permanent magnet synchronous generator (PMSG). ► A speed control strategy is developed to extract maximal wind power. ► A Fuzzy-PI speed controller is proposed to overcome the WECS nonlinearity problem. ► Simulation results show the effectiveness of the proposed control strategy. - Abstract: In this article we develop the overall model of the wind energy conversion systems (WECSs) structure based on the permanent magnet synchronous generator (PMSG), and propose a study of the electrical parts (permanent magnet synchronous machine and static converter). Our study is developed on a wind conversion system in order to produce optimum power (to extract the maximal wind power). The speed control of all machine-turbine at optimal values can provide a valuable service and useful for the management and generation of power network to which the turbine is connected. The main drawback is that the WECS is highly nonlinear, and thus a nonlinear control strategy is required. An adaptive Fuzzy-PI speed controller is proposed to overcome this problem. Simulation results are given to show the effectiveness of this control strategy. Conclusions are summarized in the last section.

  20. Adaptive control of a PWR core power using neural networks

    International Nuclear Information System (INIS)

    Arab-Alibeik, H.; Setayeshi, S.

    2005-01-01

    Reactor power control is important because of safety concerns and the call for regular and appropriate operation of nuclear power plants. It seems that the load-follow operation of these plants will be unavoidable in the future. Discrepancies between the real plant and the model used in controller design for load-follow operation encourage one to use auto-tuning and (or) adaptive techniques. Neural network technology shows great promise for addressing many problems in non-model-based adaptive control methods. Also, there has been a great attention to inverse control especially in the neural and fuzzy control context. Fortunately, online adaptation eliminates some limitations of inverse control and its shortcomings for real world applications. We use a neural adaptive inverse controller to control the power of a PWR reactor. The stability of the system and convergence of the controller parameters are guaranteed during online adaptation phase provided the controller is near the plant's real inverse after offline training period. The performance of the controller is verified using nonlinear simulations in diverse operating conditions

  1. Fuzzy controller for an uncertain dynamical system

    DEFF Research Database (Denmark)

    Kulczycki, P.; Wisniewski, Rafal

    2002-01-01

    The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters. The met......The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters....... The methodology proposed in this work may be easily adopted to other modeling uncertainties of mechanical systems, e.g. motion resistance....

  2. Fuzzy attitude control of solar sail via linear matrix inequalities

    Science.gov (United States)

    Baculi, Joshua; Ayoubi, Mohammad A.

    2017-09-01

    This study presents a fuzzy tracking controller based on the Takagi-Sugeno (T-S) fuzzy model of the solar sail. First, the T-S fuzzy model is constructed by linearizing the existing nonlinear equations of motion of the solar sail. Then, the T-S fuzzy model is used to derive the state feedback controller gains for the Twin Parallel Distributed Compensation (TPDC) technique. The TPDC tracks and stabilizes the attitude of the solar sail to any desired state in the presence of parameter uncertainties and external disturbances while satisfying actuator constraints. The performance of the TPDC is compared to a PID controller that is tuned using the Ziegler-Nichols method. Numerical simulation shows the TPDC outperforms the PID controller when stabilizing the solar sail to a desired state.

  3. Fuzzy process control and knowledge engineering in petrochemical and robotic manufacturing

    Energy Technology Data Exchange (ETDEWEB)

    Aliev, R. (Azerbaijan Industrial Univ., Dept. of Automatic Control Systems, Baku (Russia)); Aliev, F. (Azerbaijan Polytechnique Institute, Dept. of Automation and Computer Science, Baku (Russia)); Babaev, M. (Azerbaijan Industrial Univ., Laboratory of Intelligent Control Systems, Baku (Russia))

    1991-01-01

    This book presents the methodology, the functionality and the pragmatics of implementing and applying AI (Artificial Intelligence) techniques enhanced by the new mathematical discipline of fuzzy sets. Emphasis is put on the design and modelling of fuzzy controllers and intelligent control equipment for the oil processing and chemical industries, as well as on robotics and CAM (Computer-Aided Manufacturing), including the development of appropriate algorithms and computer programs. The content is strongly application-oriented in order to explain the main features of the theory of fuzzy systems using different real examples from concrete engineering projects. It excels over the present literature available on this subject by its descriptions new classes of industrial systems to be controlled with fuzzy logic, as well as by its descriptive introduction to intelligent control systems and fuzzy controllers developed and successfully implemented by the authors in working industrial plants. (orig.).

  4. HYBRID SYSTEM BASED FUZZY-PID CONTROL SCHEMES FOR UNPREDICTABLE PROCESS

    Directory of Open Access Journals (Sweden)

    M.K. Tan

    2011-07-01

    Full Text Available In general, the primary aim of polymerization industry is to enhance the process operation in order to obtain high quality and purity product. However, a sudden and large amount of heat will be released rapidly during the mixing process of two reactants, i.e. phenol and formalin due to its exothermic behavior. The unpredictable heat will cause deviation of process temperature and hence affect the quality of the product. Therefore, it is vital to control the process temperature during the polymerization. In the modern industry, fuzzy logic is commonly used to auto-tune PID controller to control the process temperature. However, this method needs an experienced operator to fine tune the fuzzy membership function and universe of discourse via trial and error approach. Hence, the setting of fuzzy inference system might not be accurate due to the human errors. Besides that, control of the process can be challenging due to the rapid changes in the plant parameters which will increase the process complexity. This paper proposes an optimization scheme using hybrid of Q-learning (QL and genetic algorithm (GA to optimize the fuzzy membership function in order to allow the conventional fuzzy-PID controller to control the process temperature more effectively. The performances of the proposed optimization scheme are compared with the existing fuzzy-PID scheme. The results show that the proposed optimization scheme is able to control the process temperature more effectively even if disturbance is introduced.

  5. Design of a Tele-Control Electrical Vehicle System Using a Fuzzy Logic Control

    Directory of Open Access Journals (Sweden)

    M. Boukhnifer

    2012-11-01

    Full Text Available This paper presents a fuzzy logic design of a tele-control electrical vehicle system. We showed that the application of fuzzy logic control allows the stability of tele-vehicle system in spite of communication delays between the operator and the vehicle. A robust bilateral controller design using fuzzy logic frameworks was proposed. This approach allows a convenient means to trade off robustness and stability for a pre-specified time-delay margin. Both the performance and robustness of the proposed method were demonstrated by simulation results for a constant time delay between the operator and the electrical vehicle system.

  6. Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

    Directory of Open Access Journals (Sweden)

    Liyang Wang

    2016-01-01

    Full Text Available Time-varying external disturbances cause instability of humanoid robots or even tip robots over. In this work, a trapezoidal fuzzy least squares support vector regression- (TF-LSSVR- based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller are collected by implementing simulation experiments. Secondly, a TF-LSSVR with a time-related trapezoidal fuzzy membership function (TFMF is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TF-LSSVR are updated using a cubature Kalman filter (CKF. Simulation results are provided. The proposed method is shown to be effective in learning and adapting occasional external disturbances and ensuring the stability margin of the robot.

  7. A fuzzy control technique for a magnetically levitated system

    Energy Technology Data Exchange (ETDEWEB)

    Lo Verso, G [C.N.R., Ce.Ri.S.E.P., Palermo (Italy); Trapanese, M [Dipt. di Ingegneria Elettrica, Univ. di Palermo (Italy)

    1996-12-31

    This paper presents the results of some analytical and numerical investigations on a control approach for magnetically leviated systems. This approach is based on fuzzy logic. It has been widely demonstrated that traditional control systems consent to maintain a stiff control on the air gap length. However, the traditional approaches could cause at very high speed, a vertical acceleration of the vehicle cabin larger than the maximum value currently allowed by the ISO standard. It is aim of this work to investigate the possibilities that a fuzzy controller offer in order to solve this problem. In order set up the controller, every mechanical degree of freedom is modelled in terms of some linguistic variables. These linguistic variables are described by several fuzzy sets. It must be noted that, doing so, the disturbances can be described in terms of fuzzy sets, too. A single-mass-model of the vehicle is considered in the paper. The features of the controller are numerically simulated under several types of disturbances and they are compared with a traditional control approach. It is shown how some parameters (especially the vertical acceleration) improve their behaviour. (orig.)

  8. Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes

    Science.gov (United States)

    Duerksen, Noel

    1997-01-01

    It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.

  9. Advanced Interval Type-2 Fuzzy Sliding Mode Control for Robot Manipulator

    Directory of Open Access Journals (Sweden)

    Ji-Hwan Hwang

    2017-01-01

    Full Text Available In this paper, advanced interval type-2 fuzzy sliding mode control (AIT2FSMC for robot manipulator is proposed. The proposed AIT2FSMC is a combination of interval type-2 fuzzy system and sliding mode control. For resembling a feedback linearization (FL control law, interval type-2 fuzzy system is designed. For compensating the approximation error between the FL control law and interval type-2 fuzzy system, sliding mode controller is designed, respectively. The tuning algorithms are derived in the sense of Lyapunov stability theorem. Two-link rigid robot manipulator with nonlinearity is used to test and the simulation results are presented to show the effectiveness of the proposed method that can control unknown system well.

  10. A new approach of active compliance control via fuzzy logic control for multifingered robot hand

    Science.gov (United States)

    Jamil, M. F. A.; Jalani, J.; Ahmad, A.

    2016-07-01

    Safety is a vital issue in Human-Robot Interaction (HRI). In order to guarantee safety in HRI, a model reference impedance control can be a very useful approach introducing a compliant control. In particular, this paper establishes a fuzzy logic compliance control (i.e. active compliance control) to reduce impact and forces during physical interaction between humans/objects and robots. Exploiting a virtual mass-spring-damper system allows us to determine a desired compliant level by understanding the behavior of the model reference impedance control. The performance of fuzzy logic compliant control is tested in simulation for a robotic hand known as the RED Hand. The results show that the fuzzy logic is a feasible control approach, particularly to control position and to provide compliant control. In addition, the fuzzy logic control allows us to simplify the controller design process (i.e. avoid complex computation) when dealing with nonlinearities and uncertainties.

  11. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    Directory of Open Access Journals (Sweden)

    Y.-M. Chiang

    2011-01-01

    Full Text Available Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  12. Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.

    Science.gov (United States)

    Mazandarani, Mehran; Pariz, Naser

    2018-05-01

    This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Novel hybrid adaptive controller for manipulation in complex perturbation environments.

    Directory of Open Access Journals (Sweden)

    Alex M C Smith

    Full Text Available In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing.

  14. Implementation of a Fuzzy Logic Speed Controller for a Permanent ...

    African Journals Online (AJOL)

    In this paper DC motor control models were mathematically extracted and implemented using fuzzy logic speed controller. All control systems suffer from problems related to undesirable overshoot, longer settling times and vibrations while going from one state to another. To overcome the maximum overshoot, fuzzy logic ...

  15. Fuzzy Logic Based Autonomous Traffic Control System

    Directory of Open Access Journals (Sweden)

    Muhammad ABBAS

    2012-01-01

    Full Text Available The aim of this paper is to design and implement fuzzy logic based traffic light Control system to solve the traffic congestion issues. In this system four input parameters: Arrival, Queue, Pedestrian and Emergency Vehicle and two output parameters: Extension in Green and Pedestrian Signals are used. Using Fuzzy Rule Base, the system extends or terminates the Green Signal according to the Traffic situation at the junction. On the presence of emergency vehicle, the system decides which signal(s should be red and how much an extension should be given to Green Signal for Emergency Vehicle. The system also monitors the density of people and makes decisions accordingly. In order to verify the proposed design algorithm MATLAB simulation is adopted and results obtained show concurrency to the calculated values according to the Mamdani Model of the Fuzzy Control System.

  16. Self-tuning fuzzy logic nuclear reactor controller

    International Nuclear Information System (INIS)

    Alang-Rashid, N. K.; Heger, A.S.

    1994-01-01

    A method for self-timing of a fuzzy logic controller (FLC) based on the estimation of the optimum value of the centroids of the its output fuzzy sets is proposed. The method can be implemented on-line and does not modify the membership function and the control rules, thus preserving the description of control statements in their original forms. Results of simulation and actual tests show that the tuning method improves the FLCs performance in following desired reactor power level trajectories (simulation tests) and simple power up and power down experiments (simulation and actual tests). The FLC control rules were derived from control statements expressing the relations between error, rate of error change, and control rod duration and direction of movements

  17. Self-tuning fuzzy logic nuclear reactor controller

    Energy Technology Data Exchange (ETDEWEB)

    Alang-Rashid, N K; Heger, A S

    1994-12-31

    A method for self-timing of a fuzzy logic controller (FLC) based on the estimation of the optimum value of the centroids of the its output fuzzy sets is proposed. The method can be implemented on-line and does not modify the membership function and the control rules, thus preserving the description of control statements in their original forms. Results of simulation and actual tests show that the tuning method improves the FLCs performance in following desired reactor power level trajectories (simulation tests) and simple power up and power down experiments (simulation and actual tests). The FLC control rules were derived from control statements expressing the relations between error, rate of error change, and control rod duration and direction of movements.

  18. Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

    Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

  19. Fuzzy batch controller for granular materials

    Directory of Open Access Journals (Sweden)

    Zamyatin Nikolaj

    2018-01-01

    Full Text Available The paper focuses on batch control of granular materials in production of building materials from fluorine anhydrite. Batching equipment is intended for smooth operation and timely feeding of supply hoppers at a required level. Level sensors and a controller of an asynchronous screw drive motor are used to control filling of the hopper with industrial anhydrite binders. The controller generates a required frequency and ensures required productivity of a feed conveyor. Mamdani-type fuzzy inference is proposed for controlling the speed of the screw that feeds mixture components. As related to production of building materials based on fluoride anhydrite, this method is used for the first time. A fuzzy controller is proven to be effective in controlling the filling level of the supply hopper. In addition, the authors determined optimal parameters of the batching process to ensure smooth operation and production of fluorine anhydrite materials of specified properties that can compete with gypsum-based products.

  20. Stability Analysis of a Type of Takagi-Sugeno PI Fuzzy Control Systems Using Circle Criterion

    Directory of Open Access Journals (Sweden)

    Kairui Cao

    2011-04-01

    Full Text Available A type of Takagi-Sugeno (T-S Proportional-Integral (PI fuzzy controllers is studied. The T-S PI fuzzy controller is formed by a T-S Proportional-Derivative (PD fuzzy controller connected with an integrator. In this particular structure, the T-S PD fuzzy controller is a weighted sum of some linear PD sub-controllers. The mathematical properties of our T-S PI fuzzy controller are also investigated. Based on these properties, the global asymptotic stability of the fuzzy control systems, in which the T-S PI fuzzy controllers are employed, are analyzed by using the well-known circle criterion. A sufficient condition with an elegant graphical interpretation in the frequency domain is further derived to guarantee the global asymptotic stability of the above fuzzy control systems. Finally, two numerical examples are provided to demonstrate how to deploy this method in analyzing the T-S PI fuzzy control systems in the frequency domain with the aid of some simple graphs.

  1. Application of a fuzzy control algorithm with improved learning speed to nuclear steam generator level control

    International Nuclear Information System (INIS)

    Park, Gee Yong; Seong, Poong Hyun

    1994-01-01

    In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection

  2. Reliable Decentralized Control of Fuzzy Discrete-Event Systems and a Test Algorithm.

    Science.gov (United States)

    Liu, Fuchun; Dziong, Zbigniew

    2013-02-01

    A framework for decentralized control of fuzzy discrete-event systems (FDESs) has been recently presented to guarantee the achievement of a given specification under the joint control of all local fuzzy supervisors. As a continuation, this paper addresses the reliable decentralized control of FDESs in face of possible failures of some local fuzzy supervisors. Roughly speaking, for an FDES equipped with n local fuzzy supervisors, a decentralized supervisor is called k-reliable (1 ≤ k ≤ n) provided that the control performance will not be degraded even when n - k local fuzzy supervisors fail. A necessary and sufficient condition for the existence of k-reliable decentralized supervisors of FDESs is proposed by introducing the notions of M̃uc-controllability and k-reliable coobservability of fuzzy language. In particular, a polynomial-time algorithm to test the k-reliable coobservability is developed by a constructive methodology, which indicates that the existence of k-reliable decentralized supervisors of FDESs can be checked with a polynomial complexity.

  3. Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects

    NARCIS (Netherlands)

    van der Spek, J.H.; Velthuis, W.J.R.; Veltink, Petrus H.; de Vries, Theodorus J.A.

    1996-01-01

    The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller

  4. Study and simulation of a MPPT controller based on fuzzy logic controller for photovoltaic system

    Energy Technology Data Exchange (ETDEWEB)

    Belaidi, R.; Chikouche, A.; Fathi, M.; Mohand Kaci, G.; Smara, Z. [Unite de Developpement des Equipements Solaires (Algeria); Haddouche, A. [Universite Badji Mokhtar (Algeria)], E-mail: rachidi3434@yahoo.fr

    2011-07-01

    With the depletion of fossil fuels and the increasing concerns about the environment, renewable energies have become more and more attractive. Photovoltaic systems convert solar energy into electric energy through the use of photovoltaic cells. The aim of this paper is to compare the capacity of fuzzy logic and perturb and observe controllers in optimizing the control performance of photovoltaic systems. Simulations were performed using Matlab and Simulink and were analyzed to determine the effectiveness of both controllers and compare them. Results showed that the fuzzy controller has a better dynamic performance than the perturb and observe controller in terms of response time and damping characteristics. In addition, the fuzzy controller was found to better follow the maximum power point and to provide faster convergence and lower statistical error. This study demonstrated that the fuzzy controller gives a better performance than traditional controllers in optimizing the performance of photovoltaic systems.

  5. A GA-fuzzy automatic generation controller for interconnected power system

    CSIR Research Space (South Africa)

    Boesack, CD

    2011-10-01

    Full Text Available This paper presents a GA-Fuzzy Automatic Generation Controller for large interconnected power systems. The design of Fuzzy Logic Controllers by means of expert knowledge have typically been the traditional design norm, however, this may not yield...

  6. Evaluation of a Multi-Variable Self-Learning Fuzzy Logic Controller ...

    African Journals Online (AJOL)

    In spite of the usefulness of fuzzy control, its main drawback comes from lack of a systematic control design methodology. The most challenging aspect of the design of a fuzzy logic controller is the elicitation of the control rules for its rule base. In this paper, a scheme capable of elicitation of acceptable rules for multivariable ...

  7. Fuzzy Logic Based MPPT Controller for a PV System

    Directory of Open Access Journals (Sweden)

    Carlos Robles Algarín

    2017-12-01

    Full Text Available The output power of a photovoltaic (PV module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.

  8. Neuro-fuzzy control of structures using acceleration feedback

    Science.gov (United States)

    Schurter, Kyle C.; Roschke, Paul N.

    2001-08-01

    This paper described a new approach for the reduction of environmentally induced vibration in constructed facilities by way of a neuro-fuzzy technique. The new control technique is presented and tested in a numerical study that involves two types of building models. The energy of each building is dissipated through magnetorheological (MR) dampers whose damping properties are continuously updated by a fuzzy controller. This semi-active control scheme relies on the development of a correlation between the accelerations of the building (controller input) and the voltage applied to the MR damper (controller output). This correlation forms the basis for the development of an intelligent neuro-fuzzy control strategy. To establish a context for assessing the effectiveness of the semi-active control scheme, responses to earthquake excitation are compared with passive strategies that have similar authority for control. According to numerical simulation, MR dampers are less effective control mechanisms than passive dampers with respect to a single degree of freedom (DOF) building model. On the other hand, MR dampers are predicted to be superior when used with multiple DOF structures for reduction of lateral acceleration.

  9. A TSK neuro-fuzzy approach for modeling highly dynamic systems

    NARCIS (Netherlands)

    Acampora, G.

    2011-01-01

    This paper introduces a new type of TSK-based neuro-fuzzy approach and its application to modeling highly dynamic systems. In details, our proposal performs an adaptive supervised learning on a collection of time series in order to create a so-called Timed Automata Based Fuzzy Controller, i.e. an

  10. Phase inductance estimation for switched reluctance motor using adaptive neuro-fuzzy inference system

    International Nuclear Information System (INIS)

    Daldaban, Ferhat; Ustkoyuncu, Nurettin; Guney, Kerim

    2006-01-01

    A new method based on an adaptive neuro-fuzzy inference system (ANFIS) for estimating the phase inductance of switched reluctance motors (SRMs) is presented. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the ANFIS. The rotor position and the phase current of the 6/4 pole SRM are used to predict the phase inductance. The phase inductance results predicted by the ANFIS are in excellent agreement with the results of the finite element method

  11. Multi-stage fuzzy PID power system automatic generation controller in deregulated environments

    International Nuclear Information System (INIS)

    Shayeghi, H.; Shayanfar, H.A.; Jalili, A.

    2006-01-01

    In this paper, a multi-stage fuzzy proportional integral derivative (PID) type controller is proposed to solve the automatic generation control (AGC) problem in a deregulated power system that operates under deregulation based on the bilateral policy scheme. In each control area, the effects of the possible contracts are treated as a set of new input signals in a modified traditional dynamical model. The multi-stage controller uses the fuzzy switch to blend a proportional derivative (PD) fuzzy logic controller with an integral fuzzy logic input. The proposed controller operates on fuzzy values passing the consequence of a prior stage on to the next stage as fact. The salient advantage of this strategy is its high insensitivity to large load changes and disturbances in the presence of plant parameter variations and system nonlinearities. This newly developed strategy leads to a flexible controller with simple structure that is easy to implement, and therefore, it can be useful for the real world power systems. The proposed method is tested on a three area power system with different contracted scenarios under various operating conditions. The results of the proposed controller are compared with those of the classical fuzzy PID type controller and classical PID controller through some performance indices to illustrate its robust performance

  12. A Modification of the Fuzzy Logic Based DASH Adaptation Scheme for Performance Improvement

    Directory of Open Access Journals (Sweden)

    Hyun Jun Kim

    2018-01-01

    Full Text Available We propose a modification of the fuzzy logic based DASH adaptation scheme (FDASH for seamless media service in time-varying network conditions. The proposed scheme (mFDASH selects a more appropriate bit-rate for the next segment by modification of the Fuzzy Logic Controller (FLC and estimates more accurate available bandwidth than FDASH scheme by using History-Based TCP Throughput Estimation. Moreover, mFDASH reduces the number of video bit-rate changes by applying Segment Bit-Rate Filtering Module (SBFM and employs Start Mechanism for clients to provide high-quality videos in the very beginning stage of the streaming service. Lastly, Sleeping Mechanism is applied to avoid any expected buffer overflow. We then use NS-3 Network Simulator to verify the performance of mFDASH. Upon the experimental results, mFDASH shows no buffer overflow within the limited buffer size, which is not guaranteed in FDASH. Also, we confirm that mFDASH provides the highest QoE to DASH clients among the three schemes (mFDASH, FDASH, and SVAA in Point-to-Point networks, Wi-Fi networks, and LTE networks, respectively.

  13. CONTROL SYSTEM DESIGN WITH FUZZY LOGIC PID-СONTROLLER TYPE 2

    Directory of Open Access Journals (Sweden)

    A. Tунік

    2011-04-01

    Full Text Available This paper presents a fuzzy logic PID-controller synthesis method for solid body guidance. Formany nonlinear systems with nonlinearities and uncertainties, the performance of fuzzy controllertype 1 may not be satisfactory. Therefore, in this work, fuzzy logic type 2 controller design isintroduced. These controllers capture the advantage of a linear controller in terms of simplicity andalso can handle nonlinearity because of their inference mechanism.The main feature of the proposedmethod constitutes in a membership functions type 2 applications. The membership function type 2is represented by upper and lower membership functions of type 1. The interval between these twofunctions represent the footprint of uncertainty, which give an opportunity to synthesize commonregulator for set of a models. The structure of fuzzy logic controller for solid body control isgrounded. Simulation results confirm the effectiveness of the proposed approach.

  14. Performance Evaluation of a PID and a Fuzzy PID Controllers Designed for Controlling a Simulated Quadcopter Rotational Dynamics Model

    Directory of Open Access Journals (Sweden)

    Laith Jasim Saud

    2017-07-01

    Full Text Available This work is concerned with designing two types of controllers, a PID and a Fuzzy PID, to be used for flying and stabilizing a quadcopter. The designed controllers have been tuned, tested, and compared using two performance indices which are the Integral Square Error (ISE and the Integral Absolute Error (IAE, and also some response characteristics like the rise time, overshoot, settling time, and the steady state error. To try and test the controllers, a quadcopter mathematical model has been developed. The model concentrated on the rotational dynamics of the quadcopter, i.e. the roll, pitch, and yaw variables. The work has been simulated with “MATLAB”. To make testing the simulated model and the controllers more realistic, the testing signals have been applied by a user through a joystick interfaced to the computer. The results obtained indicated a general superiority in performance for the Fuzzy PID controller over the PID controller used in this work. This conclusion is based by the following figures:lesser ISA for the roll, pitch, and yaw consequently, lesser IAE for the roll, pitch, and yaw consequently, lesser rise time and settling time for the roll and pitch consequently, and lesser settling time for the yaw. Moreover, the FPID gave zero overshoot versus and in the PID case for the roll, pitch, and yaw consequently. Both controllers gave zero steady state error with close rise times for the yaw. This superiority of the FPID controller is gained as the fuzzy part of it continuously and online adapts the parameters of the PID part.

  15. Autonomous Control of a Quadrotor UAV Using Fuzzy Logic

    Science.gov (United States)

    Sureshkumar, Vijaykumar

    UAVs are being increasingly used today than ever before in both military and civil applications. They are heavily preferred in "dull, dirty or dangerous" mission scenarios. Increasingly, UAVs of all kinds are being used in policing, fire-fighting, inspection of structures, pipelines etc. Recently, the FAA gave its permission for UAVs to be used on film sets for motion capture and high definition video recording. The rapid development in MEMS and actuator technology has made possible a plethora of UAVs that are suited for commercial applications in an increasingly cost effective manner. An emerging popular rotary wing UAV platform is the Quadrotor A Quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAVs are VTOL and hovering capabilities as well as a greater maneuverability. It is also simple in construction and design compared to a scaled single rotorcraft. Flying such UAVs using a traditional radio Transmitter-Receiver setup can be a daunting task especially in high stress situations. In order to make such platforms widely applicable, a certain level of autonomy is imperative to the future of such UAVs. This thesis paper presents a methodology for the autonomous control of a Quadrotor UAV using Fuzzy Logic. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. Modularity and adaptability are the key cornerstones of FLC. The objective of this thesis is to present the steps of designing, building and simulating an intelligent flight control module for a Quadrotor UAV. In the course of this research effort, a Quadrotor UAV is indigenously developed utilizing the resources of an online open source project called Aeroquad. System design is comprehensively dealt with. A math model for the Quadrotor is developed and a

  16. Fuzzy Control Model and Simulation for Nonlinear Supply Chain System with Lead Times

    Directory of Open Access Journals (Sweden)

    Songtao Zhang

    2017-01-01

    Full Text Available A new fuzzy robust control strategy for the nonlinear supply chain system in the presence of lead times is proposed. Based on Takagi-Sugeno fuzzy control system, the fuzzy control model of the nonlinear supply chain system with lead times is constructed. Additionally, we design a fuzzy robust H∞ control strategy taking the definition of maximal overlapped-rules group into consideration to restrain the impacts such as those caused by lead times, switching actions among submodels, and customers’ stochastic demands. This control strategy can not only guarantee that the nonlinear supply chain system is robustly asymptotically stable but also realize soft switching among subsystems of the nonlinear supply chain to make the less fluctuation of the system variables by introducing the membership function of fuzzy system. The comparisons between the proposed fuzzy robust H∞ control strategy and the robust H∞ control strategy are finally illustrated through numerical simulations on a two-stage nonlinear supply chain with lead times.

  17. Fuzzy control of dissolved oxygen, pH and temperature of bubble column bioreactor for Candida utilis biomass production

    Directory of Open Access Journals (Sweden)

    Hubert Arteaga Miñano

    2012-06-01

    Full Text Available An automatic control system by dissolved oxygen (DO fuzzy logic, pH and temperature in a bubble column bioreactor (BCB for Candida utilis CECT 10704 biomass production was implemented. Their performance was compared with the classical proportional control. A data acquisition card for the control was designed, built and programmed, using the 4.14 Eagle software for the design and the 3.0 Microcode Studio Plus for programming. A program in 6.0 Visual Basic, which linked up with 7.0 MatLab for fuzzy control was developed; using Mandani inference, membership functions of input and output trapezoidal and triangular; 4 rules for the DO, 3 for pH and 3 for temperature, with connector and type and for defuzzifying the centroid method. Evaluation of biomass production was performed by determining dry weight and growth kinetics with the Gompertz model.The fuzzy control performance of DO, pH and temperature showed superiority in proportional control, characterized by a very close control to set point and a low standard deviation. DO Fuzzy control at 6 ppm, pH of 6 and 30°C, allowed to have the greatest dry weight of 7.65±0.02 g/L and the highest maximum growth of 1.51±0.2, the lowest adaptation phase of 0.27±0.01 h and the greatest specific speed of Candida utilis growth rate of 0.7±0.01 h-1.

  18. Virtual reality simulation of fuzzy-logic control during underwater dynamic positioning

    Science.gov (United States)

    Thekkedan, Midhin Das; Chin, Cheng Siong; Woo, Wai Lok

    2015-03-01

    In this paper, graphical-user-interface (GUI) software for simulation and fuzzy-logic control of a remotely operated vehicle (ROV) using MATLAB™ GUI Designing Environment is proposed. The proposed ROV's GUI platform allows the controller such as fuzzy-logic control systems design to be compared with other controllers such as proportional-integral-derivative (PID) and sliding-mode controller (SMC) systematically and interactively. External disturbance such as sea current can be added to improve the modelling in actual underwater environment. The simulated results showed the position responses of the fuzzy-logic control exhibit reasonable performance under the sea current disturbance.

  19. Active Queue Management in TCP Networks Based on Fuzzy-Pid Controller

    Directory of Open Access Journals (Sweden)

    Hossein ASHTIANI

    2012-01-01

    Full Text Available We introduce a novel and robust active queue management (AQM scheme based on a fuzzy controller, called hybrid fuzzy-PID controller. In the TCP network, AQM is important to regulate the queue length by passing or dropping the packets at the intermediate routers. RED, PI, and PID algorithms have been used for AQM. But these algorithms show weaknesses in the detection and control of congestion under dynamically changing network situations. In this paper a novel Fuzzy-based proportional-integral derivative (PID controller, which acts as an active queue manager (AQM for Internet routers, is proposed. These controllers are used to reduce packet loss and improve network utilization in TCP/IP networks. A new hybrid controller is proposed and compared with traditional RED based controller. Simulations are carried out to demonstrate the effectiveness of the proposed method and show that, the new hybrid fuzzy PID controller provides better performance than random early detection (RED and PID controllers

  20. H∞ Control of Polynomial Fuzzy Systems: A Sum of Squares Approach

    OpenAIRE

    Bomo W. Sanjaya; Bambang Riyanto Trilaksono; Arief Syaichu-Rohman

    2014-01-01

    This paper proposes the control design ofa nonlinear polynomial fuzzy system with H∞ performance objective using a sum of squares (SOS) approach. Fuzzy model and controller are represented by a polynomial fuzzy model and controller. The design condition is obtained by using polynomial Lyapunov functions that not only guarantee stability but also satisfy the H∞ performance objective. The design condition is represented in terms of an SOS that can be numerically solved via the SOSTOOLS. A simul...

  1. A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network

    Science.gov (United States)

    Xu, Rui; Zhou, Miaolei

    2018-04-01

    Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.

  2. Model-based fuzzy control solutions for a laboratory Antilock Braking System

    DEFF Research Database (Denmark)

    Precup, Radu-Emil; Spataru, Sergiu; Rǎdac, Mircea-Bogdan

    2010-01-01

    This paper gives two original model-based fuzzy control solutions dedicated to the longitudinal slip control of Antilock Braking System laboratory equipment. The parallel distributed compensation leads to linear matrix inequalities which guarantee the global stability of the fuzzy control systems...

  3. Pendekatan Adaptive Neuro Fuzzy Sebagai Alternatif Bagi Bank Indonesia Dalam Menentukan Tingkat Inflasi Di Indonesia

    Directory of Open Access Journals (Sweden)

    Armaini Akhirson

    2016-10-01

    Full Text Available In uncertain economic like today, research and modeling the inflation rate is considered necessary to provide estimates and predictions of inflation rates in the future. Adaptive Neuro Fuzzy approach is a combination of  Neural Network and Fuzzy Logic. This study aims to describe the movement ofinflation(output variable  so it can beestimated by observing four Indonesia's macroeconomic data, namely the exchange rate, money supply, interbank interest rates, and the output gap (input variable. Observation period started from the data in 20011 to 20113. After the learning process is complete, fuzzy systems generate 45 fuzzy rules that can define the input-output behavior. The results of this study indicate a fairly high degree of accuracy with an average error rate is 0.5315.

  4. Good control practices underlined by an on-line fuzzy control database

    Directory of Open Access Journals (Sweden)

    Alonso, M. V.

    1994-04-01

    Full Text Available In the olive oil trade, control systems that automate extraction processes, cutting production costs and increasing processing capacity without losing quality, are always desirable. The database structure of an on-line fuzzy control of centrifugation systems and the algorithms used to attain the best control conditions are analysed. Good control practices are suggested to obtain virgin olive oil of prime quality.

    In the olive oil trade, control systems that automate extraction processes, cutting production costs and increasing processing capacity without losing quality, are always desirable. The database structure of an on-line fuzzy control of centrifugation systems and the algorithms used to attain the best control conditions are analysed. Good control practices are suggested to obtain virgin olive oil of prime quality.

  5. Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2014-01-01

    Full Text Available Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

  6. control of a dc motor using fuzzy logic control algorithm

    African Journals Online (AJOL)

    user

    controller in the control performance of an industrial type DC motor using MATLAB. The fuzzy logic .... controlled separately excited permanent magnet DC motor (PMDC). ... When the field current is constant, the flux induced by the field ...

  7. Different control applications on a vehicle using fuzzy logic control

    Indian Academy of Sciences (India)

    Vehicle vibrations; active suspensions; fuzzy logic control; vehicle model. 1. .... The general expression of the mathematical model is shown below: .... Figure 5a presents the time history of the control force when the controller exists only under.

  8. A Genetic Based Neuro-Fuzzy Controller System

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Recently, the mobile robots have great importance in the manufacturing processes. They are widely used for assembling processes, handling the dangerous components, moving the weighted things, etc. Designing the controller of the mobile robot is a very complex task. Many simple control systems used the neuro-fuzzy controller in the mobile robots. But, they faced with great complexity when moving in unstructured and dynamic environments. The proposed system introduces the uses of the genetic algorithm for optimizing the parameters of the neuro-fuzzy controller. So, the proposed system can improve the performance of the mobile robots. It has applied for a mobile robot used for moving the dangerous and critical materials in unstructured environment. Its results are compared with other traditional controller systems. The suggested system has proved its success for the real-time applications

  9. Sensitivity-based self-learning fuzzy logic control for a servo system

    NARCIS (Netherlands)

    Balenovic, M.

    1998-01-01

    Describes an experimental verification of a self-learning fuzzy logic controller (SLFLC). The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been

  10. Impulsive synchronization for Takagi-Sugeno fuzzy model and its application to continuous chaotic system

    International Nuclear Information System (INIS)

    Wang Yanwu; Guan Zhihong; Wang, Hua O.

    2005-01-01

    Recently, chaos synchronization based on T-S fuzzy model has attracted much attention because of the applicability in the case of uncertainty. In the fuzzy control scheme, linear and adaptive control methods have been introduced to solve the control problem. In this Letter, an impulsive synchronization scheme for T-S fuzzy model is developed. The proposed impulsive control scheme seems to have a simple control structure and may need less control energy than the normal continuous ones for the synchronization of T-S fuzzy system. Sufficient conditions for the impulsive synchronization are derived. The method is illustrated by applications to continuous chaotic systems and the simulation results demonstrate the effectiveness of the proposed control method

  11. Application of genetic algorithms to tuning fuzzy control systems

    Science.gov (United States)

    Espy, Todd; Vombrack, Endre; Aldridge, Jack

    1993-01-01

    Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

  12. Autonomous vehicle motion control, approximate maps, and fuzzy logic

    Science.gov (United States)

    Ruspini, Enrique H.

    1993-01-01

    Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.

  13. Simulation Study of IMC and Fuzzy Controller for HVAC System

    Directory of Open Access Journals (Sweden)

    Umamaheshwari

    2009-06-01

    Full Text Available This paper presents how the fuzzy logic controller is used to solve the control problems of complex and non linear process and show that it is more robust and their performance are less sensitive to parametric variations than conventional controllers. These systems will yield a linear response when compared to ordinary controllers. The main advantage of Fuzzy control over conventional controllers is regulation can be done without over shoot.

  14. Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor

    Science.gov (United States)

    Afiqah Zainal, Nurul; Sooi Tat, Chan; Ajisman

    2016-02-01

    Renewable energy produced by solar module gives advantages for generated three- phase induction motor in remote area. But, solar module's ou tput is uncertain and complex. Fuzzy logic controller is one of controllers that can handle non-linear system and maximum power of solar module. Fuzzy logic controller used for Maximum Power Point Tracking (MPPT) technique to control Pulse-Width Modulation (PWM) for switching power electronics circuit. DC-DC boost converter used to boost up photovoltaic voltage to desired output and supply voltage source inverter which controlled by three-phase PWM generated by microcontroller. IGBT switched Voltage source inverter (VSI) produced alternating current (AC) voltage from direct current (DC) source to control speed of three-phase induction motor from boost converter output. Results showed that, the output power of solar module is optimized and controlled by using fuzzy logic controller. Besides that, the three-phase induction motor can be drive and control using VSI switching by the PWM signal generated by the fuzzy logic controller. This concluded that the non-linear system can be controlled and used in driving three-phase induction motor.

  15. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing.

    Science.gov (United States)

    Ahmad, Muneer; Jung, Low Tan; Bhuiyan, Al-Amin

    2017-10-01

    Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary

  16. ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

  17. Research on fuzzy PID control to electronic speed regulator

    Science.gov (United States)

    Xu, Xiao-gang; Chen, Xue-hui; Zheng, Sheng-guo

    2007-12-01

    As an important part of diesel engine, the speed regulator plays an important role in stabilizing speed and improving engine's performance. Because there are so many model parameters of diesel-engine considered in traditional PID control and these parameters present non-linear characteristic.The method to adjust engine speed using traditional PID is not considered as a best way. Especially for the diesel-engine generator set. In this paper, the Fuzzy PID control strategy is proposed. Some problems about its utilization in electronic speed regulator are discussed. A mathematical model of electric control system for diesel-engine generator set is established and the way of the PID parameters in the model to affect the function of system is analyzed. And then it is proposed the differential coefficient must be applied in control design for reducing dynamic deviation of system and adjusting time. Based on the control theory, a study combined control with PID calculation together for turning fuzzy PID parameter is implemented. And also a simulation experiment about electronic speed regulator system was conducted using Matlab/Simulink and the Fuzzy-Toolbox. Compared with the traditional PID Algorithm, the simulated results presented obvious improvements in the instantaneous speed governing rate and steady state speed governing rate of diesel-engine generator set when the fuzzy logic control strategy used.

  18. Fuzzy PID control algorithm based on PSO and application in BLDC motor

    Science.gov (United States)

    Lin, Sen; Wang, Guanglong

    2017-06-01

    A fuzzy PID control algorithm is studied based on improved particle swarm optimization (PSO) to perform Brushless DC (BLDC) motor control which has high accuracy, good anti-jamming capability and steady state accuracy compared with traditional PID control. The mathematical and simulation model is established for BLDC motor by simulink software, and the speed loop of the fuzzy PID controller is designed. The simulation results show that the fuzzy PID control algorithm based on PSO has higher stability, high control precision and faster dynamic response speed.

  19. Statistical Methods for Fuzzy Data

    CERN Document Server

    Viertl, Reinhard

    2011-01-01

    Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy m

  20. Stable Hovering Flight for a Small Unmanned Helicopter Using Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Arbab Nighat Khizer

    2014-01-01

    Full Text Available Stable hover flight control for small unmanned helicopter under light air turbulent environment is presented. Intelligent fuzzy logic is chosen because it is a nonlinear control technique based on expert knowledge and is capable of handling sensor created noise and contradictory inputs commonly encountered in flight control. The fuzzy nonlinear control utilizes these distinct qualities for attitude, height, and position control. These multiple controls are developed using two-loop control structure by first designing an inner-loop controller for attitude angles and height and then by establishing outer-loop controller for helicopter position. The nonlinear small unmanned helicopter model used comes from X-Plane simulator. A simulation platform consisting of MATLAB/Simulink and X-Plane© flight simulator was introduced to implement the proposed controls. The main objective of this research is to design computationally intelligent control laws for hovering and to test and analyze this autopilot for small unmanned helicopter model on X-Plane under ideal and mild turbulent condition. Proposed fuzzy flight controls are validated using an X-Plane helicopter model before being embedded on actual helicopter. To show the effectiveness of the proposed fuzzy control method and its ability to cope with the external uncertainties, results are compared with a classical PD controller. Simulated results show that two-loop fuzzy controllers have a good ability to establish stable hovering for a class of unmanned rotorcraft in the presence of light turbulent environment.

  1. A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller

    Science.gov (United States)

    Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi

    This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.

  2. Two-Dimensional Fuzzy Sliding Mode Control of a Field-Sensed Magnetic Suspension System

    Directory of Open Access Journals (Sweden)

    Jen-Hsing Li

    2014-01-01

    Full Text Available This paper presents the two-dimensional fuzzy sliding mode control of a field-sensed magnetic suspension system. The fuzzy rules include both the sliding manifold and its derivative. The fuzzy sliding mode control has advantages of the sliding mode control and the fuzzy control rules are minimized. Magnetic suspension systems are nonlinear and inherently unstable systems. The two-dimensional fuzzy sliding mode control can stabilize the nonlinear systems globally and attenuate chatter effectively. It is adequate to be applied to magnetic suspension systems. New design circuits of magnetic suspension systems are proposed in this paper. ARM Cortex-M3 microcontroller is utilized as a digital controller. The implemented driver, sensor, and control circuits are simpler, more inexpensive, and effective. This apparatus is satisfactory for engineering education. In the hands-on experiments, the proposed control scheme markedly improves performances of the field-sensed magnetic suspension system.

  3. Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm

    Science.gov (United States)

    Yu, Gwo-Ruey; Huang, Yu-Chia; Cheng, Chih-Yung

    2016-07-01

    In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi-Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.

  4. Development of neural network driven fuzzy controller for outlet sodium temperature of DHX

    International Nuclear Information System (INIS)

    Okusa, Kyoichi; Endou, Akira; Yoshikawa, Shinji; Ozawa, Kenji

    1996-01-01

    Fuzzy controls are capable to exquisitely control non-linear dynamic systems in wide operating range, using linguistic description to define the control law. However the selection and the definition of the fuzzy rules and sets require a tedious trial and error process based on experience. As a method to overcome this limitation, a neural network driven fuzzy control (NDF), where the learning capability of the neural network (NN) is used to build the fuzzy rules and sets, is presented in this paper. In the NDF control the IF part of a fuzzy control is represented by a multilayer NN while the THEN part is represented by a series of multilayer NNs which calculate the desirable control action. In this work the usual stepwise variable reduction method, used for the selection of the input variable in the THEN part NN, is replaced with a learning algorithm with forgetting mechanism that realizes the automatic reduction of the variables and the tuning up of all the fuzzy control law i.e. the membership function. The NDF has been successfully applied to control the outlet sodium temperature of a dump heat exchanger (DHX) of a FBR plant

  5. Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach

    Directory of Open Access Journals (Sweden)

    Rana Dinesh Singh

    2015-01-01

    Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.

  6. Fuzzy modeling and control of the calcination process in a kiln

    International Nuclear Information System (INIS)

    Ramirez, M.; Haber, R.

    1999-01-01

    Calcination kilns are strongly nonlinear, multivariable processes, that only can be modeled with great uncertainty. In order to get a quality product and ensure the process efficiency, the controller must keep a prescribed temperature profile optimizing the fuel consumption. In this paper, a design methodology of a multivariable fuzzy controller for a nickel calcination kiln is presented. The controller structure is a classical one, and uses the Mamdani fuzzy inference system. In simulation results the fuzzy controller exhibits a great robustness in presence of several types of disturbances, and a better performance than the PID in same conditions is observed. (author)

  7. A modeling of fuzzy logic controller on gamma scanning device

    International Nuclear Information System (INIS)

    Arjoni Amir

    2010-01-01

    Modeling and simulation of controller to set the high position and direction of the source of gamma radiation isotope Co-60 and Nal(TL) detector of gamma scanning device by using fuzzy logic controller FLC have been done. The high positions and in the right direction of gamma radiation and Nal (TI) detector obtained the optimal enumeration. The counting data obtained from gamma scanning device counting system is affected by the instability of high position and direction of the gamma radiation source and Nal(TI) detector or the height and direction are not equal between the gamma radiation source and Nal(TI) detector. Assumed a high position and direction of radiation sources can be fixed while the high position detector h (2, 1,0, -1, -2) can be adjusted up and down and the detector can be changed direction to the left or right angle ω (2, 1 , 0, -1, -2) when the position and direction are no longer aligned with the direction of the source of gamma radiation, the counting results obtained will not be optimal. Movement detector direction towards the left or right and the high detector arranged by the DC motor using fuzzy logic control in order to obtain the amount of output fuzzy logic control which forms the optimal output quantity count. The variation of height difference h between the source position of the gamma radiation detector and change direction with the detector angle ω becomes the input variable membership function (member function) whereas the fuzzy logic for the output variable membership function of fuzzy logic control output is selected scale fuzzy logic is directly proportional to the amount of optimal counting. From the simulation results obtained by the relationship between the amount of data output variable of fuzzy logic controller and the amount of data input variable height h and direction detector ω is depicted in graphical form surface. (author)

  8. A STRUCTURE APPROACH FOR A PHOTOVOLTAIC STATION CONTROL BASED ON ADAPTIVE FUZZY AGENT

    Directory of Open Access Journals (Sweden)

    I. A. Elzein

    2017-01-01

    Full Text Available The solar energy is directly converted into electrical energy by solar PV module. Each type of PV module has its own specific characteristic corresponding to the surrounding condition such as irradiation, and temperature and this makes the tracking of maximum power point (MPP a complicated problem. To overcome this problem, many maximum power point tracking (MPPT control algorithms have been presented. Fuzzy logic (FL has been used for tracking the MPP of PV modules because it has the advantages of being robust, relatively simple to design and does not require the knowledge of an exact model where a mathematical model of the PV module, DC-DC converter, are used in the study of FL based MPPT algorithm. It is suggested to present this problem in the form of two-folds; first to identify the deviation of the power to maximum power point, and secondly, to control the voltage of the DC-DC converter corresponding to maximum power. In this paper, the first discussion approach will stress out the integration of model predictive control in maximum power point tracking MPPT and as progressing a second approach is identified as fuzzy logic controller FLC and perturb & Observe P&O algorithms are analyzed. All are interrelated to MPPT model for a photovoltaic module, PVM, to search for and generate the maximum power; in this case what’s called P-max. As per the first technique the focus is on the optimal duty ratio, D, for a series of multi diverse types of converters and load matching. The design of the MPPT for a stand-alone photovoltaic power generation system is applied where the system will consist of a solar array with nonlinear time varying characteristics, and a converter with appropriate filters. The integration of model predictive control will be addressed first in this paper. The second fold will implement an MPPT system that use the FLC and compare it with a classical MPPT P&O algorithm through the utilization of Simulink. The novel design in

  9. PI and Fuzzy Control Strategies for High Voltage Output DC-DC Boost Power Converter - Hardware Implementation and Analysis

    DEFF Research Database (Denmark)

    Padmanaban, Sanjeevi Kumar; Blaabjerg, Frede; Siano, Pierluigi

    2016-01-01

    This paper presents the control strategies by Proportional-Integral (P-I) and Fuzzy Logic (FL) for a DC-DC boost power converter for high output voltage configuration. Standard DC-DC converters are traditionally used for high voltage direct current (HVDC) power transmission systems. But, lack its...... converter with inbuilt voltage-lift technique and overcome the aforementioned deficiencies. Further, the control strategy is adapted based on proportional-integral (P-I) and fuzzy logic, closed-loop controller to regulate the outputs and ensure the performances. Complete hardware prototype of EHV converter...... performances in terms of efficiency, reduced transfer gain and increased cost with sensor units. Moreover, the internal self-parasitic components reduce the output voltage and efficiency of classical high voltage converters (HVC). This investigation focused on extra high-voltage (EHV) DC-DC boost power...

  10. Using adaptive network based fuzzy inference system to forecast regional electricity loads

    International Nuclear Information System (INIS)

    Ying, L.-C.; Pan, M.-C.

    2008-01-01

    Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads

  11. Using adaptive network based fuzzy inference system to forecast regional electricity loads

    Energy Technology Data Exchange (ETDEWEB)

    Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

    2008-02-15

    Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

  12. FUZZY LOGIC CONTROL OF ELECTRIC MOTORS AND MOTOR DRIVES: FEASIBILITY STUDY

    Science.gov (United States)

    The report gives results of a study (part 1) of fuzzy logic motor control (FLMC). The study included: 1) reviews of existing applications of fuzzy logic, of motor operation, and of motor control; 2) a description of motor control schemes that can utilize FLMC; 3) selection of a m...

  13. Simulation research on multivariable fuzzy model predictive control of nuclear power plant

    International Nuclear Information System (INIS)

    Su Jie

    2012-01-01

    To improve the dynamic control capabilities of the nuclear power plant, the algorithm of the multivariable nonlinear predictive control based on the fuzzy model was applied in the main parameters control of the nuclear power plant, including control structure and the design of controller in the base of expounding the math model of the turbine and the once-through steam generator. The simulation results show that the respond of the change of the gas turbine speed and the steam pressure under the algorithm of multivariable fuzzy model predictive control is faster than that under the PID control algorithm, and the output value of the gas turbine speed and the steam pressure under the PID control algorithm is 3%-5% more than that under the algorithm of multi-variable fuzzy model predictive control. So it shows that the algorithm of multi-variable fuzzy model predictive control can control the output of the main parameters of the nuclear power plant well and get better control effect. (author)

  14. Variable structure TITO fuzzy-logic controller implementation for a solar air-conditioning system

    Energy Technology Data Exchange (ETDEWEB)

    Lygouras, J.N.; Pachidis, Th. [Laboratory of Electronics, School of Electrical and Computer Engineering, Democritus University of Thrace, GR-67100 Xanthi (Greece); Kodogiannis, V.S. [Centre for Systems Analysis, School of Computer Science, University of Westminster, London HA1 3TP (United Kingdom); Tarchanidis, K.N. [Department of Petroleum Technology, Technological Education Institute of Kavala, GR-65404, Kavala (Greece); Koukourlis, C.S. [Laboratory of Telecommunications, School of Electrical and Computer Engineering, Democritus University of Thrace, GR-67100 Xanthi (Greece)

    2008-04-15

    The design and implementation of a Two-Input/Two-Output (TITO) variable structure fuzzy-logic controller for a solar-powered air-conditioning system is described in this paper. Two DC motors are used to drive the generator pump and the feed pump of the solar air-conditioner. The first affects the temperature in the generator of the solar air-conditioner, while the second, the pressure in the power loop. The difficulty of Multi-Input/Multi-Output (MIMO) systems control is how to overcome the coupling effects among each degree of freedom. First, a traditional fuzzy-controller has been designed, its output being one of the components of the control signal for each DC motor driver. Secondly, according to the characteristics of the system's dynamics coupling, an appropriate coupling fuzzy-controller (CFC) is incorporated into a traditional fuzzy-controller (TFC) to compensate for the dynamic coupling among each degree of freedom. This control strategy simplifies the implementation problem of fuzzy control, but can also improve the control performance. This mixed fuzzy controller (MFC) can effectively improve the coupling effects of the systems, and this control strategy is easy to design and implement. Experimental results from the implemented system are presented. (author)

  15. Wavelet-fuzzy speed indirect field oriented controller for three-phase AC motor drive – Investigation and implementation

    Directory of Open Access Journals (Sweden)

    Sanjeevikumar Padmanaban

    2016-09-01

    Full Text Available Three-phase voltage source inverter driven induction motor is used in many medium- and high-power applications. Precision in speed of the motor play vital role, i.e. popular methods of direct/indirect field-oriented control (FOC are applied. FOC is employed with proportional–integral (P-I or proportional–integral–derivative (P-I-D controllers and they are not adaptive, since gains are fixed at all operating conditions. Therefore, it needs a robust speed controlling in precision for induction motor drive application. This research paper articulates a novel speed control for FOC induction motor drive based on wavelet-fuzzy logic interface system. In specific, the P-I-D controller of IFOC which is actually replaced by the wavelet-fuzzy controller. The speed feedback (error signal is composed of multiple low and high frequency components. Further, these components are decomposed by the discrete wavelet transform and the fuzzy logic controller to generate the scaled gains for the indirect FOC induction motor. Complete model of the proposed ac motor drive is developed with numerical simulation Matlab/Simulink software and tested under different working conditions. For experimental verification, a hardware prototype was implemented and the control algorithm is framed using TMS320F2812 digital signal processor (dsp. Both simulation and hardware results presented in this paper are shown in close agreement and conformity about the suitability for industrial applications.

  16. Direct Torque Control System for Permanent Magnet Synchronous Machine with Fuzzy Speed Pi Regulator

    Science.gov (United States)

    Nabti, K.; Abed, K.; Benalla, H.

    2008-06-01

    The Permanent Magnet Synchronous Machine (PMSM) speed regulation with a conventional PI regulator reduces the speed control precision, increase the torque fluctuation, and consequentially low performances of the whole system. With utilisation of fuzzy logic method, this paper presents the self adaptation of conventional PI regulator parameters Kp and Ki (proportional and integral coefficients respectively), using to regulate the speed in Direct Torque Control strategy (DTC). The ripples of both torque and flux are reduced remarkable, small overshooting and good dynamic of the speed and torque. Simulation results verify the proposed method validity.

  17. LFC based adaptive PID controller using ANN and ANFIS techniques

    Directory of Open Access Journals (Sweden)

    Mohamed I. Mosaad

    2014-12-01

    Full Text Available This paper presents an adaptive PID Load Frequency Control (LFC for power systems using Neuro-Fuzzy Inference Systems (ANFIS and Artificial Neural Networks (ANN oriented by Genetic Algorithm (GA. PID controller parameters are tuned off-line by using GA to minimize integral error square over a wide-range of load variations. The values of PID controller parameters obtained from GA are used to train both ANFIS and ANN. Therefore, the two proposed techniques could, online, tune the PID controller parameters for optimal response at any other load point within the operating range. Testing of the developed techniques shows that the adaptive PID-LFC could preserve optimal performance over the whole loading range. Results signify superiority of ANFIS over ANN in terms of performance measures.

  18. Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

    Institute of Scientific and Technical Information of China (English)

    杨海; 李威; 罗成名

    2015-01-01

    Pure inertial navigation system (INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network (WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter (KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system (FIS), and the fuzzy adaptive Kalman filter (FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

  19. Fuzzy logic controller for crude oil levels at Escravos Tank Farm ...

    African Journals Online (AJOL)

    Fuzzy logic controller (FLC) for crude oil flow rates and tank levels was designed for monitoring flow and tank level management at Escravos Tank Farm in Nigeria. The fuzzy control system incorporated essence of expert knowledge required to handle the tasks. Proportional Integral Derivative (PID) control of crude flow ...

  20. Power-Constrained Fuzzy Logic Control of Video Streaming over a Wireless Interconnect

    Science.gov (United States)

    Razavi, Rouzbeh; Fleury, Martin; Ghanbari, Mohammed

    2008-12-01

    Wireless communication of video, with Bluetooth as an example, represents a compromise between channel conditions, display and decode deadlines, and energy constraints. This paper proposes fuzzy logic control (FLC) of automatic repeat request (ARQ) as a way of reconciling these factors, with a 40% saving in power in the worst channel conditions from economizing on transmissions when channel errors occur. Whatever the channel conditions are, FLC is shown to outperform the default Bluetooth scheme and an alternative Bluetooth-adaptive ARQ scheme in terms of reduced packet loss and delay, as well as improved video quality.

  1. Fuzzy – PI controller to control the velocity parameter of Induction Motor

    Science.gov (United States)

    Malathy, R.; Balaji, V.

    2018-04-01

    The major application of Induction motor includes the usage of the same in industries because of its high robustness, reliability, low cost, highefficiency and good self-starting capability. Even though it has the above mentioned advantages, it also have some limitations: (1) the standard motor is not a true constant-speed machine, itsfull-load slip varies less than 1 % (in high-horsepower motors).And (2) it is not inherently capable of providing variable-speedoperation. In order to solve the above mentioned problem smart motor controls and variable speed controllers are used. Motor applications involve non linearity features, which can be controlled by Fuzzy logic controller as it is capable of handling those features with high efficiency and it act similar to human operator. This paper presents individuality of the plant modelling. The fuzzy logic controller (FLC)trusts on a set of linguistic if-then rules, a rule-based Mamdani for closed loop Induction Motor model. Themotor model is designed and membership functions are chosenaccording to the parameters of the motor model. Simulation results contains non linearity in induction motor model. A conventional PI controller iscompared practically to fuzzy logic controller using Simulink.

  2. Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor

    International Nuclear Information System (INIS)

    Zainal, Nurul Afiqah; Tat, Chan Sooi; Ajisman

    2016-01-01

    Renewable energy produced by solar module gives advantages for generated three- phase induction motor in remote area. But, solar module's output is uncertain and complex. Fuzzy logic controller is one of controllers that can handle non-linear system and maximum power of solar module. Fuzzy logic controller used for Maximum Power Point Tracking (MPPT) technique to control Pulse-Width Modulation (PWM) for switching power electronics circuit. DC-DC boost converter used to boost up photovoltaic voltage to desired output and supply voltage source inverter which controlled by three-phase PWM generated by microcontroller. IGBT switched Voltage source inverter (VSI) produced alternating current (AC) voltage from direct current (DC) source to control speed of three-phase induction motor from boost converter output. Results showed that, the output power of solar module is optimized and controlled by using fuzzy logic controller. Besides that, the three-phase induction motor can be drive and control using VSI switching by the PWM signal generated by the fuzzy logic controller. This concluded that the non-linear system can be controlled and used in driving three-phase induction motor. (paper)

  3. The Temperature Fuzzy Control System of Barleythe Malt Drying Based on Microcontroller

    Science.gov (United States)

    Gao, Xiaoyang; Bi, Yang; Zhang, Lili; Chen, Jingjing; Yun, Jianmin

    The control strategy of temperature and humidity in the beer barley malt drying chamber based on fuzzy logic control was implemented.Expounded in this paper was the selection of parameters for the structure of the regulatory device, as well as the essential design from control rules based on the existing experience. A temperature fuzzy controller was thus constructed using relevantfuzzy logic, and humidity control was achieved by relay, ensured the situation of the humidity to control the temperature. The temperature's fuzzy control and the humidity real-time control were all processed by single chip microcomputer with assembly program. The experimental results showed that the temperature control performance of this fuzzy regulatory system,especially in the ways of working stability and responding speed and so on,was better than normal used PID control. The cost of real-time system was inquite competitive position. It was demonstrated that the system have a promising prospect of extensive application.

  4. Analysis, control and design of a non-inverting buck-boost converter: A bump-less two-level T-S fuzzy PI control.

    Science.gov (United States)

    Almasi, Omid Naghash; Fereshtehpoor, Vahid; Khooban, Mohammad Hassan; Blaabjerg, Frede

    2017-03-01

    In this paper, a new modified fuzzy Two-Level Control Scheme (TLCS) is proposed to control a non-inverting buck-boost converter. Each level of fuzzy TLCS consists of a tuned fuzzy PI controller. In addition, a Takagi-Sugeno-Kang (TSK) fuzzy switch proposed to transfer the fuzzy PI controllers to each other in the control system. The major difficulty in designing fuzzy TLCS which degrades its performance is emerging unwanted drastic oscillations in the converter output voltage during replacing the controllers. Thereby, the fuzzy PI controllers in each level of TLCS structure are modified to eliminate these oscillations and improve the system performance. Some simulations and digital signal processor based experiments are conducted on a non-inverting buck-boost converter to support the effectiveness of the proposed TLCS in controlling the converter output voltage. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  5. A Fuzzy Rule-based Controller For Automotive Vehicle Guidance

    OpenAIRE

    Hessburg, Thomas; Tomizuka, Masayoshi

    1991-01-01

    A fuzzy rule-based controller is applied to lateral guidance of a vehicle for an automated highway system. The fuzzy rules, based on human drivers' experiences, are developed to track the center of a lane in the presence of external disturbances and over a range of vehicle operating conditions.

  6. Prediction of Mechanical Properties of LDPE-TPS Nanocomposites Using Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Maryam Sabetzadeh

    2012-12-01

    Full Text Available The changes in the behaviour of mechanical properties of low densitypolyethylene-thermoplastic corn starch (LDPE-TPCS nanocompositeswere studied by an adaptive neuro-fuzzy interference system. LDPE-TPCScomposites containing different quantities of nanoclay (Cloisite®15A, 0.5-3wt. % were prepared by extrusion process. In practice, it is difficult to carry out several experiments to identify the relationship between the extrusion process parameters and mechanical properties of the nanocomposites. In this paper, an adaptive neuro-fuzzy inference system (ANFIS was used for non-linear mapping between the processingparameters and the mechanical properties of LDPE-TPCS nanocomposites. ANFIS model due to possessing inference ability of fuzzy systems and also the learning feature of neural networks, could be used as a multiple inputs-multiple outputs to predict mechanical properties (such as ultimate tensile strength, elongation-at-break, Young’s modulus and relative impact strength of the nanocomposites. The proposed ANFIS model utilizes temperature, torque and Cloisite®15A contents as input parameters to predict the desired mechanical properties. The results obtained in this work indicatedthat ANFIS is an effective and intelligent method for prediction of the mechanical properties of the LDPE-TPCS nanocomposites with a good accuracy. The statistical quality of the ANFIS model was significant due to its acceptable mean square error criterion and good correlation coefficient (values > 0.8 between the experimental and simulated outputs.

  7. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

    Science.gov (United States)

    Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

  8. The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs

    Science.gov (United States)

    Wu, Bing-Fei; Ma, Li-Shan; Perng, Jau-Woei

    This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

  9. Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

    Science.gov (United States)

    Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

    2017-04-01

    We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its

  10. Fuzzy-Genetic Optimal Control for Four Degreeof Freedom Robotic Arm Movement

    OpenAIRE

    V. K. Banga; R. Kumar; Y. Singh

    2009-01-01

    In this paper, we present optimal control for movement and trajectory planning for four degrees-of-freedom robot using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs) for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like; Movement, Friction and Settling Time in robotic arm movement have been compensated using Fuzzy logic and Genetic Algorithms. The development of a fuzzy genetic optimizatio...

  11. Fuzzy control with random delays using invariant cones and its application to control of energy processes in microelectromechanical motion devices

    Energy Technology Data Exchange (ETDEWEB)

    Sinha, A.S.C. [Purdue Univ., Indianapolis, IN (United States). Dept. of Electrical Engineering; Lyshevski, S. [Rochester Inst. of Technology, NY (United States)

    2005-05-01

    In this paper, a class of microelectromechanical systems described by nonlinear differential equations with random delays is examined. Robust fuzzy controllers are designed to control the energy conversion processes with the ultimate objective to guarantee optimal achievable performance. The fuzzy rule base used consists of a collection of r fuzzy IF-THEN rules defined as a function of the conditional variable. The method of the theory of cones and Lyapunov functionals is used to design a class of local fuzzy control laws. A verifiably sufficient condition for stochastic stability of fuzzy stochastic microelectromechanical systems is given. As an example, we have considered the design of a fuzzy control law for an electrostatic micromotor. (author)

  12. Fuzzy control with random delays using invariant cones and its application to control of energy processes in microelectromechanical motion devices

    International Nuclear Information System (INIS)

    Sinha, A.S.C.; Lyshevski, S.

    2005-01-01

    In this paper, a class of microelectromechanical systems described by nonlinear differential equations with random delays is examined. Robust fuzzy controllers are designed to control the energy conversion processes with the ultimate objective to guarantee optimal achievable performance. The fuzzy rule base used consists of a collection of r fuzzy IF-THEN rules defined as a function of the conditional variable. The method of the theory of cones and Lyapunov functionals is used to design a class of local fuzzy control laws. A verifiably sufficient condition for stochastic stability of fuzzy stochastic microelectromechanical systems is given. As an example, we have considered the design of a fuzzy control law for an electrostatic micromotor

  13. Control de velocidad de un motor de inducción utilizando un controlador Fuzzy

    Directory of Open Access Journals (Sweden)

    Madelín Lima Álvarez

    2011-03-01

    Full Text Available Se realiza la simulación del sistema de control de velocidad del motor de inducción. Se parte del enfoquevectorial y de un sistema de coordenadas adecuado a su futura utilización, utilizando para esto el lenguaje desimulación de sistemas dinámicos Simulink. En cuanto a la simulación del método de control vectorial porcampo orientado se ha empleado el denominado método de control vectorial indirecto. El control de la velocidaddel motor se simuló utilizando un controlador Fuzzy, comparándose posteriormente estos resultados con losobtenidos mediante la utilización de un controlador PI digital.  This work carries out the simulation of speed control of induction motor. It is based on the field orientation theoryand on a system of coordinates adapted to its future use. The language that has been used for the simulation ofdynamics systems is Simulink. The indirect method of the field orientation control theory was used in themathematical analysis of the induction motor. The speed control of the induction motor was simulated using aFuzzy controller and these results were compared with those obtained by means of the use of a PI digitalcontroller.

  14. Fuzzy logic controller for weaning neonates from mechanical ventilation.

    OpenAIRE

    Hatzakis, G. E.; Davis, G. M.

    2002-01-01

    Weaning from mechanical ventilation is the gradual detachment from any ventilatory support till normal spontaneous breathing can be fully resumed. To date, we have developed a fuzzy logic controller for weaning COPD adults using pressure support ventilation (PS). However, adults and newborns differ in the pathophysiology of lung disease. We therefore used our fuzzy logic-based weaning platform to develop modularized components for weaning newborns with lung disease. Our controller uses the he...

  15. Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system.

    Science.gov (United States)

    Aqil, M; Kita, I; Yano, A; Nishiyama, S

    2006-01-01

    It is widely accepted that an efficient flood alarm system may significantly improve public safety and mitigate economical damages caused by inundations. In this paper, a modified adaptive neuro-fuzzy system is proposed to modify the traditional neuro-fuzzy model. This new method employs a rule-correction based algorithm to replace the error back propagation algorithm that is employed by the traditional neuro-fuzzy method in backward pass calculation. The final value obtained during the backward pass calculation using the rule-correction algorithm is then considered as a mapping function of the learning mechanism of the modified neuro-fuzzy system. Effectiveness of the proposed identification technique is demonstrated through a simulation study on the flood series of the Citarum River in Indonesia. The first four-year data (1987 to 1990) was used for model training/calibration, while the other remaining data (1991 to 2002) was used for testing the model. The number of antecedent flows that should be included in the input variables was determined by two statistical methods, i.e. autocorrelation and partial autocorrelation between the variables. Performance accuracy of the model was evaluated in terms of two statistical indices, i.e. mean average percentage error and root mean square error. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach, and evolving graphical features, and can be adopted for any similar situation to predict the streamflow. The main data processing includes gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood data, to train/test the model using various input options, and to visualize results. The program code consists of a set of files, which can be modified as well to match other

  16. High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

    Science.gov (United States)

    Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

    2008-02-01

    Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

  17. Improvement of Frequency Fluctuations in Microgrids Using an Optimized Fuzzy P-PID Controller by Modified Multi Objective Gravitational Search Algorithm

    Directory of Open Access Journals (Sweden)

    H. Shayeghi

    2016-12-01

    Full Text Available Microgrids is an new opportunity to reduce the total costs of power generation and supply the energy demands through small-scale power plants such as wind sources, photo voltaic panels, battery banks, fuel cells, etc. Like any power system in micro grid (MG, an unexpected faults or load shifting leads to frequency oscillations. Hence, this paper employs an adaptive fuzzy P-PID controller for frequency control of microgrid and a modified multi objective Chaotic Gravitational Search Algorithm (CGSA in order to find out the optimal setting parameters of the proposed controller. To provide a robust controller design, two non-commensurable objective functions are formulated based on eigenvalues-domain and time-domain and multi objective CGSA algorithm is used to solve them. Moreover, a fuzzy decision method is applied to extract the best and optimal Pareto fronts. The proposed controller is carried out on a MG system under different loading conditions with wind turbine generators, photovoltaic system, flywheel energy, battery storages, diesel generator and electrolyzer. The simulation results revealed that the proposed controller is more stable in comparison with the classical and other types of fuzzy controller.

  18. Internet and Fuzzy Based Control System for Rotary Kiln in Cement Manufacturing Plant

    Directory of Open Access Journals (Sweden)

    Hanane Zermane

    2017-01-01

    Full Text Available This paper develops an Internet-based fuzzy control system for an industrial process plant to ensure the remote and fuzzy control in cement factories in Algeria. The remote process consists of control, diagnosing alarms occurs, maintaining and synchronizing different regulation loops. Fuzzy control of the kiln ensures that the system be operational at all times, with minimal downtime. Internet technology ensures remote control. The system reduces downtimes and can guided by operators in the main control room or via Internet.

  19. An adaptive neuro fuzzy model for estimating the reliability of component-based software systems

    Directory of Open Access Journals (Sweden)

    Kirti Tyagi

    2014-01-01

    Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.

  20. Performance assessment of electric power generations using an adaptive neural network algorithm and fuzzy DEA

    Energy Technology Data Exchange (ETDEWEB)

    Javaheri, Zahra

    2010-09-15

    Modeling, evaluating and analyzing performance of Iranian thermal power plants is the main goal of this study which is based on multi variant methods analysis. These methods include fuzzy DEA and adaptive neural network algorithm. At first, we determine indicators, then data is collected, next we obtained values of ranking and efficiency by Fuzzy DEA, Case study is thermal power plants In view of the fact that investment to establish on power plant is very high, and maintenance of power plant causes an expensive expenditure, moreover using fossil fuel effected environment hence optimum produce of current power plants is important.

  1. Wireless Intelligent Monitoring and Control System of Greenhouse Temperature Based on Fuzzy-PID

    Directory of Open Access Journals (Sweden)

    Mei ZHAN

    2014-03-01

    Full Text Available Control effect is not ideal for traditional control method and wired control system, since greenhouse temperature has such characteristics as nonlinear and longtime lag. Therefore, Fuzzy- PID control method was introduced and radio frequency chip CC1110 was applied to design greenhouse wireless intelligent monitoring and control system. The design of the system, the component of nodes and the developed intelligent management software system were explained in this paper. Then describe the design of the control algorithm Fuzzy-PID. By simulating the new method in Matlab software, the results showed that Fuzzy-PID method small overshoot and better dynamic performance compared with general PID control. It has shorter settling time and no steady-state error compared with fuzzy control. It can meet requirements in greenhouse production.

  2. Implementation Of Automatic Wiper Speed Control And Headlight Modes Control Systems Using Fuzzy Logic

    OpenAIRE

    ThetKoKo; ZawMyoTun; Hla Myo Tun

    2015-01-01

    Abstract This research paper describes the design and simulation of the automatic wiper speed and headlight modes controllers using fuzzy logic. This proposed system consists of a fuzzy logic controller to control a cars wiper speed and headlight modes. The automatic wiper system detects the rain and its intensity. And according to the rain intensity the wiper speed is automatically controlled. Headlight modes automatically changes either from low beam mode to high beam mode or form high beam...

  3. Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)

    International Nuclear Information System (INIS)

    Kakar, Manish; Nystroem, Haakan; Aarup, Lasse Rye; Noettrup, Trine Jakobi; Olsen, Dag Rune

    2005-01-01

    The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude

  4. Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)

    Energy Technology Data Exchange (ETDEWEB)

    Kakar, Manish [Department of Radiation Biology, Norwegian Radium Hospital, Montebello, 0310 Oslo (Norway); Nystroem, Haakan [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Aarup, Lasse Rye [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Noettrup, Trine Jakobi [Department of Radiation Oncology, The Finsen Centre, Rigshospitalet, Copenhagen (Denmark); Olsen, Dag Rune [Department of Radiation Biology, Norwegian Radium Hospital, Montebello, 0310 Oslo (Norway); Department of Medical Physics and Technology, Norwegian Radium Hospital, Oslo (Norway); Department of Physics, University of Oslo (Norway)

    2005-10-07

    The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.

  5. A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO

    Directory of Open Access Journals (Sweden)

    Mehdi Neshat

    2015-11-01

    Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.

  6. Polynomial fuzzy model-based control systems stability analysis and control synthesis using membership function dependent techniques

    CERN Document Server

    Lam, Hak-Keung

    2016-01-01

    This book presents recent research on the stability analysis of polynomial-fuzzy-model-based control systems where the concept of partially/imperfectly matched premises and membership-function dependent analysis are considered. The membership-function-dependent analysis offers a new research direction for fuzzy-model-based control systems by taking into account the characteristic and information of the membership functions in the stability analysis. The book presents on a research level the most recent and advanced research results, promotes the research of polynomial-fuzzy-model-based control systems, and provides theoretical support and point a research direction to postgraduate students and fellow researchers. Each chapter provides numerical examples to verify the analysis results, demonstrate the effectiveness of the proposed polynomial fuzzy control schemes, and explain the design procedure. The book is comprehensively written enclosing detailed derivation steps and mathematical derivations also for read...

  7. A Mamdani Adaptive Neural Fuzzy Inference System for Improvement of Groundwater Vulnerability.

    Science.gov (United States)

    Agoubi, Belgacem; Dabbaghi, Radhia; Kharroubi, Adel

    2018-01-23

    Assessing groundwater vulnerability is an important procedure for sustainable water management. Various methods have been developed for effective assessment of groundwater vulnerability and protection. However, each method has its own conditions of use and, in practice; it is difficult to return the same results for the same site. The research conceptualized and developed an improved DRASTIC method using Mamdani Adaptive Neural Fuzzy Inference System (M-ANFIS-DRASTIC). DRASTIC and M-ANFIS-DRASTIC were applied in the Jorf aquifer, southeastern Tunisia, and results were compared. Results confirm that M-ANFIS-DRASTIC combined with geostatistical tools is more powerful, generated more precise vulnerability classes with very low estimation variance. Fuzzy logic has a power to produce more realistic aquifer vulnerability assessments and introduces new ways of modeling in hydrogeology using natural human language expressed by logic rules. © 2018, National Ground Water Association.

  8. Fuzzy logic controller for stabilization of biped robot gait

    Directory of Open Access Journals (Sweden)

    Ryadchikov I.V.

    2018-01-01

    Full Text Available The article centers round the problem of stabilization of biped robot gait through smoothing out the jumps of first and second order derivatives of a biped robot control vector using the fuzzy logic approach. The structure of a composite Takagi-Sugeno fuzzy logic controller developed by the authors is presented. The simulation study of a robot gait with climbing an obstacle is carried out and the results provided in the article showed that the developed controller performed significantly better than the analytical formula model in terms of smoothing out the derivatives of the control vector.

  9. On Modeling the Behavior of Comparators for Complex Fuzzy Objects in a Fuzzy Object-Relational Database Management System

    Directory of Open Access Journals (Sweden)

    JuanM. Medina

    2012-08-01

    Full Text Available This paper proposes a parameterized definition for fuzzy comparators on complex fuzzy datatypes like fuzzy collections with conjunctive semantics and fuzzy objects. This definition and its implementation on a Fuzzy Object-Relational Database Management System (FORDBMS provides the designer with a powerful tool to adapt the behavior of these operators to the semantics of the considered application.

  10. Fuzzy PID Feedback Control of Piezoelectric Actuator with Feedforward Compensation

    Directory of Open Access Journals (Sweden)

    Ziqiang Chi

    2014-01-01

    Full Text Available Piezoelectric actuator is widely used in the field of micro/nanopositioning. However, piezoelectric hysteresis introduces nonlinearity to the system, which is the major obstacle to achieve a precise positioning. In this paper, the Preisach model is employed to describe the hysteresis characteristic of piezoelectric actuator and an inverse Preisach model is developed to construct a feedforward controller. Considering that the analytical expression of inverse Preisach model is difficult to derive and not suitable for practical application, a digital inverse model is established based on the input and output data of a piezoelectric actuator. Moreover, to mitigate the compensation error of the feedforward control, a feedback control scheme is implemented using different types of control algorithms in terms of PID control, fuzzy control, and fuzzy PID control. Extensive simulation studies are carried out using the three kinds of control systems. Comparative investigation reveals that the fuzzy PID control system with feedforward compensation is capable of providing quicker response and better control accuracy than the other two ones. It provides a promising way of precision control for piezoelectric actuator.

  11. Embedded intelligent adaptive PI controller for an electromechanical system.

    Science.gov (United States)

    El-Nagar, Ahmad M

    2016-09-01

    In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Innovative teaching tools of automatic control and evaluation of trainees’s mathematical knowledge using fuzzy logic

    Directory of Open Access Journals (Sweden)

    Светлана Николаевна Дворяткина

    2014-12-01

    Full Text Available This article focuses on the actual problem of designing information systems of automated control of mathematical knowledge of students using fuzzy logic, which take into account the shortcomings of modern systems of evaluation and control. These include a limited number of forms of response and two-point scoring system, inflexible procedures calculating the final assessment, the lack of consideration of estimating the depth and breadth of knowledge, adaptation of the estimation procedure to the individual characteristics of the students.

  13. Study of Noise Canceling Performance of Feedforward Fuzzy-Based ANC System under Non-Causal Condition

    DEFF Research Database (Denmark)

    Mojallali, Hamed; Izadi-Zamanabadi, Roozbeh; Amini, Rouzbeh

    of noise canceling performance of feed-forward fuzzy-based ANC systems for ducts under non-causal condition is presented. For this purpose, we use fuzzy filtered-x algorithm as an adaptive filter and the results are compared with classical filteredx algorithm which is employed under the same conditions......Feed-forward active noise control (ANC) systems act as adaptive systems to control and cancel undesired signals and noises. If the delay in the noise canceling subsystems increases more than the delays in the primary path, non-causal condition will occur in these systems. In this paper, study....... Analysis shows that ANC systems using fuzzy algorithm has better efficiency for noise cancellation in non-causal condition....

  14. Research on Coordinated Robotic Motion Control Based on Fuzzy Decoupling Method in Fluidic Environments

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2014-01-01

    Full Text Available The underwater recovery of autonomous underwater vehicles (AUV is a process of 6-DOF motion control, which is related to characteristics with strong nonlinearity and coupling. In the recovery mission, the vehicle requires high level control accuracy. Considering an AUV called BSAV, this paper established a kinetic model to describe the motion of AUV in the horizontal plane, which consisted of nonlinear equations. On the basis of this model, the main coupling variables were analyzed during recovery. Aiming at the strong coupling problem between the heading control and sway motion, we designed a decoupling compensator based on the fuzzy theory and the decoupling theory. We analyzed to the rules of fuzzy compensation, the input and output membership functions of fuzzy compensator, through compose operation and clear operation of fuzzy reasoning, and obtained decoupling compensation quantity. Simulation results show that the fuzzy decoupling controller effectively reduces the overshoot of the system, and improves the control precision. Through the water tank experiments and analysis of experimental data, the effectiveness and feasibility of AUV recovery movement coordinated control based on fuzzy decoupling method are validated successful, and show that the fuzzy decoupling control method has a high practical value in the recovery mission.

  15. Adaptive Fuzzy Control for Power-Frequency Characteristic Regulation in High-RES Power Systems

    Directory of Open Access Journals (Sweden)

    Evangelos Rikos

    2017-07-01

    Full Text Available Future power systems control will require large-scale activation of reserves at distribution level. Despite their high potential, distributed energy resources (DER used for frequency control pose challenges due to unpredictability, grid bottlenecks, etc. To deal with these issues, this study presents a novel strategy of power frequency characteristic dynamic adjustment based on the imbalance state. This way, the concerned operators become aware of the imbalance location but also a more accurate redistribution of responsibilities in terms of reserves activations is achieved. The proposed control is based on the concept of “cells” which are power systems with operating capabilities and responsibilities similar to control areas (CAs, but fostering the use of resources at all voltage levels, particularly distribution grids. Control autonomy of cells allows increased RES hosting. In this study, the power frequency characteristic of a cell is adjusted in real time by means of a fuzzy controller, which curtails part of the reserves, in order to avoid unnecessary deployment throughout a synchronous area, leading to a more localised activation and reducing losses, congestions and reserves exhaustion. Simulation tests in a four-cell reference power system prove that the controller significantly reduces the use of reserves without compromising the overall stability.

  16. A systematic approach for fine-tuning of fuzzy controllers applied to WWTPs

    DEFF Research Database (Denmark)

    Ruano, M.V.; Ribes, J.; Sin, Gürkan

    2010-01-01

    A systematic approach for fine-tuning fuzzy controllers has been developed and evaluated for an aeration control system implemented in a WWTR The challenge with the application of fuzzy controllers to WWTPs is simply that they contain many parameters, which need to be adjusted for different WWTP ...

  17. A Study on Application of Fuzzy Adaptive Unscented Kalman Filter to Nonlinear Turbojet Engine Control

    Science.gov (United States)

    Han, Dongju

    2018-05-01

    Safe and efficient flight powered by an aircraft turbojet engine relies on the performance of the engine controller preventing compressor surge with robustness from noises or disturbances. This paper proposes the effective nonlinear controller associated with the nonlinear filter for the real turbojet engine with highly nonlinear dynamics. For the feasible controller study the nonlinearity of the engine dynamics was investigated by comparing the step responses from the linearized model with the original nonlinear dynamics. The fuzzy-based PID control logic is introduced to control the engine efficiently and FAUKF is applied for robustness from noises. The simulation results prove the effectiveness of FAUKF applied to the proposed controller such that the control performances are superior over the conventional controller and the filer performance using FAUKF indicates the satisfactory results such as clearing the defects by reducing the distortions without compressor surge, whereas the conventional UKF is not fully effective as occurring some distortions with compressor surge due to a process noise.

  18. Selected Aircraft Throttle Controller With Support Of Fuzzy Expert Inference System

    Directory of Open Access Journals (Sweden)

    Żurek Józef

    2014-12-01

    Full Text Available The paper describes Zlin 143Lsi aircraft engine work parameters control support method – hourly fuel flow as a main factor under consideration. The method concerns project of aircraft throttle control support system with use of fuzzy logic (fuzzy inference. The primary purpose of the system is aircraft performance optimization, reducing flight cost at the same time and support proper aircraft engine maintenance. Matlab Software and Fuzzy Logic Toolbox were used in the project. Work of the system is presented with use of twenty test samples, five of them are presented graphically. In addition, system control surface, included in the paper, supports system all work range analysis.

  19. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Dong Yun Kim; Poong Hyun Seong; .

    1997-01-01

    In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)

  20. Fuzzy batch controller for granular materials

    OpenAIRE

    Zamyatin Nikolaj; Smirnov Gennadij; Fedorchuk Yuri; Rusina Olga

    2018-01-01

    The paper focuses on batch control of granular materials in production of building materials from fluorine anhydrite. Batching equipment is intended for smooth operation and timely feeding of supply hoppers at a required level. Level sensors and a controller of an asynchronous screw drive motor are used to control filling of the hopper with industrial anhydrite binders. The controller generates a required frequency and ensures required productivity of a feed conveyor. Mamdani-type fuzzy infer...