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Sample records for mode fuzzy control

  1. 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.

  2. FUZZY SLIDING MODE CONTROLLER FOR DOUBLY FED ...

    African Journals Online (AJOL)

    2010-12-31

    Dec 31, 2010 ... against internal and external perturbations, but the FSMC is superior to ... controller, doubly fed induction motor, fuzzy logic control. 1. ... capabilities in accounting for modeling imprecision and bounded disturbances. It ..... To show the effect of the parameters uncertainties, we have simulated the system with.

  3. Fuzzy sliding mode controller for doubly fed induction motor speed ...

    African Journals Online (AJOL)

    The use of the nonlinear fuzzy sliding mode method provides very good performance for motor operation and robustness of the control law despite the external/internal perturbations. The chattering effects is eliminated by a particular function "sat" that presents a serious problem to applications of variable structure systems.

  4. 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...

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

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    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.

  6. Two-Dimensional Fuzzy Sliding Mode Control of a Field-Sensed Magnetic Suspension System

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    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.

  7. Implementation Of Automatic Wiper Speed Control And Headlight Modes Control Systems Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    ThetKoKo

    2015-07-01

    Full Text Available 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 mode to low beam mode depending on the light intensity from the other vehicle coming from the opposite direction. The system comprises of PIC impedance sensor piezoelectric vibration sensor LDR headlamps and a DC motor to accurate the windshield wiper. Piezoelectric sensor is used to detect the rain intensity which is based on the piezoelectric effect. MATLAB software is used to achieve the designed goal.

  8. A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

    Science.gov (United States)

    Ozkop, Emre; Altas, Ismail H.; Okumus, H. Ibrahim; Sharaf, Adel M.

    2015-11-01

    In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional-integral-derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.

  9. 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.

  10. 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)

  11. Advanced Interval Type-2 Fuzzy Sliding Mode Control for Robot Manipulator

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    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.

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

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    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.

  13. Design Sliding Mode Controller of with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator

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    Farzin Piltan

    2013-06-01

    Full Text Available Sliding mode controller (SMC is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness.  In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.

  14. Fuzzy sliding mode control for maximum power point tracking of a photovoltaic pumping system

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    Sabah Miqoi

    2017-03-01

    Full Text Available In this paper a new maximum power point tracking method based on fuzzy sliding mode control is proposed, and employed in a PV water pumping system based on a DC-DC boost converter, to produce maximum power from the solar panel hence more speed in the DC motor and more water quantity. This method combines two different tracking techniques sliding mode control and fuzzy logic; our controller is based on sliding mode control, then to give better stability and enhance the power production a fuzzy logic technique was added. System modeling, sliding method definition and the new control method presentation are represented in this paper. The results of the simulation that are compared to both sliding mode controller and perturbation and observation method demonstrate effectiveness and robustness of the proposed controller.

  15. Chattering-free fuzzy sliding-mode control strategy for uncertain chaotic systems

    International Nuclear Information System (INIS)

    Yau, H.-T.; Chen, C.-L.

    2006-01-01

    This paper proposes a chattering-free fuzzy sliding-mode control (FSMC) strategy for uncertain chaotic systems. A fuzzy logic control is used to replace the discontinuous sign function of the reaching law in traditional sliding-mode control (SMC), and hence a control input without chattering is obtained in the chaotic systems with uncertainties. Base on the Lyapunov stability theory, we address the design schemes of integration fuzzy sliding-mode control, where the reaching law is proposed by a set of linguistic rules and the control input is chattering free. The Genesio chaotic system is used to test the proposed control strategy and the simulation results show the FSMC not only can control the uncertain chaotic behaviors to a desired state without oscillator very fast, but also the switching function is smooth without chattering. This result implies that this strategy is feasible and effective for chaos control

  16. Position Control of the Single Spherical Wheel Mobile Robot by Using the Fuzzy Sliding Mode Controller

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    Hamed Navabi

    2017-01-01

    Full Text Available A spherical wheel robot or Ballbot—a robot that balances on an actuated spherical ball—is a new and recent type of robot in the popular area of mobile robotics. This paper focuses on the modeling and control of such a robot. We apply the Lagrangian method to derive the governing dynamic equations of the system. We also describe a novel Fuzzy Sliding Mode Controller (FSMC implemented to control a spherical wheel mobile robot. The nonlinear nature of the equations makes the controller nontrivial. We compare the performance of four different fuzzy controllers: (a regulation with one signal, (b regulation and position control with one signal, (c regulation and position control with two signals, and (d FSMC for regulation and position control with two signals. The system is evaluated in a realistic simulation and the robot parameters are chosen based on a LEGO platform, so the designed controllers have the ability to be implemented on real hardware.

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

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    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.

  18. Position Control of the Single Spherical Wheel Mobile Robot by Using the Fuzzy Sliding Mode Controller

    OpenAIRE

    Hamed Navabi; Soroush Sadeghnejad; Sepehr Ramezani; Jacky Baltes

    2017-01-01

    A spherical wheel robot or Ballbot—a robot that balances on an actuated spherical ball—is a new and recent type of robot in the popular area of mobile robotics. This paper focuses on the modeling and control of such a robot. We apply the Lagrangian method to derive the governing dynamic equations of the system. We also describe a novel Fuzzy Sliding Mode Controller (FSMC) implemented to control a spherical wheel mobile robot. The nonlinear nature of the equations makes the controller nontrivi...

  19. 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.

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

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    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.

  1. Development and comparison of integrated dynamics control systems with fuzzy logic control and sliding mode control

    International Nuclear Information System (INIS)

    Song, Jeong Hoon

    2013-01-01

    In this study, four integrated dynamics control (IDC) systems abbreviated as IDCB, IDCS, IDCF, and IDCR are developed, evaluated and compared. IDC systems were integrated with brake and steer control systems to enhance lateral stability and handling performance. To construct the IDC systems, a vehicle model with fourteen degrees of freedom, a fuzzy logic controller, and a sliding mode ABS controller were used. They were tested with various steering inputs when excessive full brake pressure or no brake pressure was applied on dry asphalt, wet asphalt, a snow-covered paved road, and a split-µ road. The results showed that an IDC-equipped vehicle improved lateral stability and controllability in every driving condition compared to an ABS-equipped vehicle. Under all road conditions, IDC controllers enabled the yaw rate to follow the reference yaw rate almost perfectly and reduced the body slip angle. On a split-µ road, IDCB, IDCS, IDCF, and IDCR vehicles drove straight ahead with only very small deviations.

  2. Implementation of fuzzy-sliding mode based control of a grid connected photovoltaic system.

    Science.gov (United States)

    Menadi, Abdelkrim; Abdeddaim, Sabrina; Ghamri, Ahmed; Betka, Achour

    2015-09-01

    The present work describes an optimal operation of a small scale photovoltaic system connected to a micro-grid, based on both sliding mode and fuzzy logic control. Real time implementation is done through a dSPACE 1104 single board, controlling a boost chopper on the PV array side and a voltage source inverter (VSI) on the grid side. The sliding mode controller tracks permanently the maximum power of the PV array regardless of atmospheric condition variations, while The fuzzy logic controller (FLC) regulates the DC-link voltage, and ensures via current control of the VSI a quasi-total transit of the extracted PV power to the grid under a unity power factor operation. Simulation results, carried out via Matlab-Simulink package were approved through experiment, showing the effectiveness of the proposed control techniques. Copyright © 2015. Published by Elsevier Ltd.

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

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    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.

  4. Predictive Sliding Mode Control for Attitude Tracking of Hypersonic Vehicles Using Fuzzy Disturbance Observer

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    Xianlei Cheng

    2015-01-01

    Full Text Available We propose a predictive sliding mode control (PSMC scheme for attitude control of hypersonic vehicle (HV with system uncertainties and external disturbances based on an improved fuzzy disturbance observer (IFDO. First, for a class of uncertain affine nonlinear systems with system uncertainties and external disturbances, we propose a predictive sliding mode control based on fuzzy disturbance observer (FDO-PSMC, which is used to estimate the composite disturbances containing system uncertainties and external disturbances. Afterward, to enhance the composite disturbances rejection performance, an improved FDO-PSMC (IFDO-PSMC is proposed by incorporating a hyperbolic tangent function with FDO to compensate for the approximate error of FDO. Finally, considering the actuator dynamics, the proposed IFDO-PSMC is applied to attitude control system design for HV to track the guidance commands with high precision and strong robustness. Simulation results demonstrate the effectiveness and robustness of the proposed attitude control scheme.

  5. 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.

  6. 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).

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

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    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.

  8. Robust sliding mode control for uncertain servo system using friction observer and recurrent fuzzy neural networks

    International Nuclear Information System (INIS)

    Han, Seong Ik; Jeong, Chan Se; Yang, Soon Yong

    2012-01-01

    A robust positioning control scheme has been developed using friction parameter observer and recurrent fuzzy neural networks based on the sliding mode control. As a dynamic friction model, the LuGre model is adopted for handling friction compensation because it has been known to capture sufficiently the properties of a nonlinear dynamic friction. A developed friction parameter observer has a simple structure and also well estimates friction parameters of the LuGre friction model. In addition, an approximation method for the system uncertainty is developed using recurrent fuzzy neural networks technology to improve the precision positioning degree. Some simulation and experiment provide the verification on the performance of a proposed robust control scheme

  9. Fuzzy sliding mode controller of a pneumatic active isolating system using negative stiffness structure

    International Nuclear Information System (INIS)

    Le, Thanh Danh; Ahn, Kyoung Kwan

    2012-01-01

    A novel active vibration isolation system using negative stiffness structure (active system with NSS) for low excitation frequency ranges (< 5 Hz) is developed successfully. Here, the negative stiffness structure (NSS) is used to minimize the attraction of vibration. Then, the fuzzy sliding mode controller (FSMC) is designed to improve the vibration isolation performance of the active system with NSS. Based on Lyapunov stability theorem, the fuzzy control rules are constructed. Next, the experimental apparatus is built for evaluating the isolation efficiency of the proposed system controlled by the FSMC corresponding to various excitation conditions. In addition, the isolation performance of the active system with NSS, the active system without NSS and the passive the system with NSS is compared. The experimental results confirmed that the active system with NSS gives better isolation efficiency than the active system without NSS and the passive system with NSS in low excitation frequency areas

  10. 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.

  11. Robust sliding mode control for uncertain servo system using friction observer and recurrent fuzzy neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Han, Seong Ik [Pusan National University, Busan (Korea, Republic of); Jeong, Chan Se; Yang, Soon Yong [University of Ulsan, Ulsan (Korea, Republic of)

    2012-04-15

    A robust positioning control scheme has been developed using friction parameter observer and recurrent fuzzy neural networks based on the sliding mode control. As a dynamic friction model, the LuGre model is adopted for handling friction compensation because it has been known to capture sufficiently the properties of a nonlinear dynamic friction. A developed friction parameter observer has a simple structure and also well estimates friction parameters of the LuGre friction model. In addition, an approximation method for the system uncertainty is developed using recurrent fuzzy neural networks technology to improve the precision positioning degree. Some simulation and experiment provide the verification on the performance of a proposed robust control scheme.

  12. Enhancement of micro-grid performance during islanding mode using storage batteries and new fuzzy logic pitch angle controller

    International Nuclear Information System (INIS)

    Kamel, Rashad M.; Chaouachi, A.; Nagasaka, Ken

    2011-01-01

    Research highlights: → Novel fuzzy pitch angle controller is proposed for smoothing wind fluctuation. → Storage batteries are used for performance improve of MG in islanding mode. → Those new techniques are compared with conventional PI pitch angle controller. -- Abstract: Power system deregulation, shortage of transmission capacities and needing to reduce green house gas have led to increase interesting in distributed generations (DGs) especially renewable sources. This study developed a complete model able to analysis and simulates in details the transient dynamic performance of the Micro-Grid (MG) during and subsequent islanding process. Wind speed fluctuations cause high fluctuations in output power of wind turbine which lead to fluctuations of frequency and voltages of the MG during the islanding mode. In this paper a new fuzzy logic pitch angle controller is proposed to smooth the output power of wind turbine to reduce MG frequency and voltage fluctuations during the islanding mode. The proposed fuzzy logic pitch controller is compared with the conventional PI pitch angle controller which usually used for wind turbine power control. Results proved the effectiveness of the proposed fuzzy controller in improvement of the MG performance. Also, this paper proposed using storage batteries technique to reduce the frequency deviation and fluctuations originated from wind power solar power fluctuations. Results indicate that the storage batteries technique is superior than fuzzy logic pitch controller in reducing frequency deviation, but with more expensive than the fuzzy controller. All models and controllers are built using Matlab (registered) Simulink (registered) environment.

  13. Comparison of Sliding Mode Control and Fuzzy Logic control applied to Variable Speed Wind Energy Conversion Systems

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    Souhila Rached Zine

    2015-08-01

    Full Text Available wind energy features prominently as a supplementary energy booster. It does not pollute and is inexhaustible. However, its high cost is a major constraint, especially on the less windy sites. The purpose of wind energy systems is to maximize energy efficiency, and extract maximum power from the wind speed. In This case, the MPPT control becomes important. To realize this control, strategy conventional Proportional and Integral (PI controller is usually used. However, this strategy cannot achieve better performance. This paper proposes other control methods of a turbine which optimizes its production such as fuzzy logic, sliding mode control. These methods improve the quality and energy efficiency. The proposed Sliding Mode Control (SMC strategy and the fuzzy controllers have presented attractive features such as robustness to parametric uncertainties of the turbine, simplicity of its design and good performances. The simulation result under Matlab\\Simulink has validated the performance of the proposed MPPT strategies.

  14. High performance fuzzy sliding mode control of DFIG supplied by seven level NPC inverter

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    Mohamed BENKAHLA

    2017-09-01

    Full Text Available This article presents the powers control of a variable speed wind turbine (WT based on a doubly fed induction generator (DFIG because of their advantages in terms of economy and control. The considered system consists of a double fed induction generator whose stator is connected directly to the network and its rotor is supplied by seven-level inverter with structure NPC are well used to minimize the harmonics absorbed by the DFIG. In order to control the power flowing between the stator of the DFIG and the grid. Have been studied and compared two types of controllers : Sliding Mode Control (SMC and Fuzzy SMC (FSMC. Their respective performances are compared in terms of power reference tracking, response to sudden speed variations, sensitivity to perturbations and robustness against machine parameters variations.

  15. Fuzzy Sliding Mode Lateral Control of Intelligent Vehicle Based on Vision

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    Linhui Li

    2013-01-01

    Full Text Available The lateral control of intelligent vehicle is studied in this paper, with the intelligent vehicle DLUIV-1 based on visual navigation as the object of research. Firstly, the lateral control model based on visual preview is established. The kinematics model based on visual preview, including speed and other factors, is used to calculate the lateral error and direction error. Secondly, according to the characteristics of lateral control, an efficient strategy of intelligent vehicle lateral mode is proposed. The integration of the vehicle current lateral error and direction error is chosen as the parameter of the sliding mode switching function to design the sliding surface. The control variables are adjusted according to the fuzzy control rules to ensure that they meet the existence and reaching condition. The sliding mode switching function is regarded as the control objective, to ensure the stability of the steering wheel rotation. Simulation results show that the lateral controller can guarantee high path-tracking accuracy and strong robustness for the change of model parameters.

  16. Application of Fuzzy Algorithm in Optimizing Hierarchical Sliding Mode Control for Pendubot System

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    Xuan Dung Huynh

    2017-12-01

    Full Text Available Pendubot is a classical under-actuated SIMO model for control algorithm testing in laboratory of universities. In this paper, authors design a fuzzy-sliding control for this system. The controller is designed from a new idea of application of fuzzy algorithm for optioning control parameters. The response of system on TOP position under fuzzysliding control algorithm is proved to be better than under sliding controller through Matlab/Simulink simulation.

  17. Motorized CPM/CAM physiotherapy device with sliding-mode Fuzzy Neural Network control loop.

    Science.gov (United States)

    Ho, Hung-Jung; Chen, Tien-Chi

    2009-11-01

    Continuous passive motion (CPM) and controllable active motion (CAM) physiotherapy devices promote rehabilitation of damaged joints. This paper presents a computerized CPM/CAM system that obviates the need for mechanical resistance devices such as springs. The system is controlled by a computer which performs sliding-mode Fuzzy Neural Network (FNN) calculations online. CAM-type resistance force is generated by the active performance of an electric motor which is controlled so as to oppose the motion of the patient's leg. A force sensor under the patient's foot on the device pedal provides data for feedback in a sliding-mode FNN control loop built around the motor. Via an active impedance control feedback system, the controller drives the motor to behave similarly to a damped spring by generating and controlling the amplitude and direction of the pedal force in relation to the patient's leg. Experiments demonstrate the high sensitivity and speed of the device. The PC-based feedback nature of the control loop means that sophisticated auto-adaptable CPM/CAM custom-designed physiotherapy becomes possible. The computer base also allows extensive data recording, data analysis and network-connected remote patient monitoring.

  18. 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.

  19. An improved efficiency of fuzzy sliding mode control of permanent magnet synchronous motor for wind turbine generator pumping system

    International Nuclear Information System (INIS)

    Benchabane, F.; Titaouine, A.; Guettaf, A.; Yahia, K.; Taibi, D.; Bennis, O.

    2012-01-01

    This paper presents an analysis by which the dynamic performances of a permanent magnet synchronous motor (PMSM) motor is controlled through a hysteresis current loop and an outer speed loop with different controllers. The dynamics of the wind turbine pumping drive system with (PI) and a fuzzy sliding mode (FSM) speed controllers are presented. In order to optimize the overall system efficiency, a maximum power point tracker is also used. Simulation is carried out by formatting the mathematical model for wind turbine generator, motor and pump load. The results for such complicated and nonlinear system, with fuzzy sliding mode speed controller show improvement in transient response of the PMSM drive over conventional PI. The effectiveness of the FSM controller is also demonstrated. (author)

  20. Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller

    OpenAIRE

    Jingang Guo; Xiaoping Jian; Guangyu Lin

    2014-01-01

    Traditional friction braking torque and motor braking torque can be used in braking for electric vehicles (EVs). A sliding mode controller (SMC) based on the exponential reaching law for the anti-lock braking system (ABS) is developed to maintain the optimal slip value. Parameter optimizing is applied to the reaching law by fuzzy logic control (FLC). A regenerative braking algorithm, in which the motor torque is taken full advantage of, is adopted to distribute the braking force between the m...

  1. Tracking Control of a Magnetic Shape Memory Actuator Using an Inverse Preisach Model with Modified Fuzzy Sliding Mode Control

    Directory of Open Access Journals (Sweden)

    Jhih-Hong Lin

    2016-08-01

    Full Text Available Magnetic shape memory (MSM alloys are a new class of smart materials with extraordinary strains up to 12% and frequencies in the range of 1 to 2 kHz. The MSM actuator is a potential device which can achieve high performance electromagnetic actuation by using the properties of MSM alloys. However, significant non-linear hysteresis behavior is a significant barrier to control the MSM actuator. In this paper, the Preisach model was used, by capturing experiments from different input signals and output responses, to model the hysteresis of MSM actuator, and the inverse Preisach model, as a feedforward control, provided compensational signals to the MSM actuator to linearize the hysteresis non-linearity. The control strategy for path tracking combined the hysteresis compensator and the modified fuzzy sliding mode control (MFSMC which served as a path controller. Based on the experimental results, it was verified that a tracking error in the order of micrometers was achieved.

  2. Tracking Control of a Magnetic Shape Memory Actuator Using an Inverse Preisach Model with Modified Fuzzy Sliding Mode Control.

    Science.gov (United States)

    Lin, Jhih-Hong; Chiang, Mao-Hsiung

    2016-08-25

    Magnetic shape memory (MSM) alloys are a new class of smart materials with extraordinary strains up to 12% and frequencies in the range of 1 to 2 kHz. The MSM actuator is a potential device which can achieve high performance electromagnetic actuation by using the properties of MSM alloys. However, significant non-linear hysteresis behavior is a significant barrier to control the MSM actuator. In this paper, the Preisach model was used, by capturing experiments from different input signals and output responses, to model the hysteresis of MSM actuator, and the inverse Preisach model, as a feedforward control, provided compensational signals to the MSM actuator to linearize the hysteresis non-linearity. The control strategy for path tracking combined the hysteresis compensator and the modified fuzzy sliding mode control (MFSMC) which served as a path controller. Based on the experimental results, it was verified that a tracking error in the order of micrometers was achieved.

  3. Fully automatic control of paraplegic FES pedaling using higher-order sliding mode and fuzzy logic control.

    Science.gov (United States)

    Farhoud, Aidin; Erfanian, Abbas

    2014-05-01

    In this paper, a fully automatic robust control strategy is proposed for control of paraplegic pedaling using functional electrical stimulation (FES). The method is based on higher-order sliding mode (HOSM) control and fuzzy logic control. In FES, the strength of muscle contraction can be altered either by varying the pulse width (PW) or by the pulse amplitude (PA) of the stimulation signal. The proposed control strategy regulates simultaneously both PA and PW (i.e., PA/PW modulation). A HOSM controller is designed for regulating the PW and a fuzzy logic controller for the PA. The proposed control scheme is free-model and does not require any offline training phase and subject-specific information. Simulation studies on a virtual patient and experiments on three paraplegic subjects demonstrate good tracking performance and robustness of the proposed control strategy against muscle fatigue and external disturbances during FES-induced pedaling. The results of simulation studies show that the power and cadence tracking errors are 5.4% and 4.8%, respectively. The experimental results indicate that the proposed controller can improve pedaling system efficacy and increase the endurance of FES pedaling. The average of power tracking error over three paraplegic subjects is 7.4±1.4% using PA/PW modulation, while the tracking error is 10.2±1.2% when PW modulation is used. The subjects could pedal for 15 min with about 4.1% power loss at the end of experiment using proposed control strategy, while the power loss is 14.3% using PW modulation. The controller could adjust the stimulation intensity to compensate the muscle fatigue during long period of FES pedaling.

  4. Free-flying dynamics and control of an astronaut assistant robot based on fuzzy sliding mode algorithm

    Science.gov (United States)

    Gao, Qing; Liu, Jinguo; Tian, Tongtong; Li, Yangmin

    2017-09-01

    Space robots can perform some tasks in harsh environment as assistants of astronauts or substitutions of astronauts. Taking the limited working time and the arduous task of the astronauts in the space station into account, an astronaut assistant robot (AAR-2) applied in the space station is proposed and designed in this paper. The AAR-2 is achieved with some improvements on the basis of AAR-1 which was designed before. It can exploit its position and attitude sensors and control system to free flight or hover in the space cabin. And it also has a definite environmental awareness and artificial intelligence to complete some specified tasks under the control of astronauts or autonomously. In this paper, it mainly analyzes and controls the 6-DOF motion of the AAR-2. Firstly, the system configuration of AAR-2 is specifically described, and the movement principles are analyzed. Secondly, according to the physical model of the AAR-2, the Newton - Euler equation is applied in the preparation of space dynamics model of 6-DOF motion. Then, according to the mathematical model's characteristics which are nonlinear and strong coupling, a dual closed loop position and attitude controller based on fuzzy sliding mode control is proposed and designed. Finally, simulation experiments are appropriate to provide for AAR-2 control system by using Matlab/Simulink. From the simulation results it can be observed that the designed fuzzy sliding mode controller can control the 6-DOF motion of AAR-2 quickly and precisely.

  5. 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...

  6. 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....

  7. 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.

  8. Dual mode linguistic hedge fuzzy logic controller for an isolated wind-diesel hybrid power system with superconducting magnetic energy storage unit

    International Nuclear Information System (INIS)

    Thameem Ansari, M.Md.; Velusami, S.

    2010-01-01

    A design of dual mode linguistic hedge fuzzy logic controller for an isolated wind-diesel hybrid power system with superconducting magnetic energy storage unit is proposed in this paper. The design methodology of dual mode linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of linguistic hedges and hybrid genetic algorithm-simulated annealing algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically and can speed up the control result to fit the system demand. The hybrid genetic algorithm-simulated annealing algorithm is adopted to search the optimal linguistic hedge combination in the linguistic hedge module. Dual mode concept is also incorporated in the proposed controller because it can improve the system performance. The system with the proposed controller was simulated and the frequency deviation resulting from a step load disturbance is presented. The comparison of the proportional plus integral controller, fuzzy logic controller and the proposed dual mode linguistic hedge fuzzy logic controller shows that, with the application of the proposed controller, the system performance is improved significantly. The proposed controller is also found to be less sensitive to the changes in the parameters of the system and also robust under different operating modes of the hybrid power system.

  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. Regulation of Blood Glucose Concentration in Type 1 Diabetics Using Single Order Sliding Mode Control Combined with Fuzzy On-line Tunable Gain, a Simulation Study

    OpenAIRE

    Dinani, Soudabeh Taghian; Zekri, Maryam; Kamali, Marzieh

    2015-01-01

    Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and...

  11. 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

  12. Lyapunov Based-Distributed Fuzzy-Sliding Mode Control for Building Integrated-DC Microgrid with Plug-in Electric Vehicle

    DEFF Research Database (Denmark)

    Ghiasi, Mohammad Iman; Aliakbar Golkar, Masoud; Hajizadeh, Amin

    2017-01-01

    This paper presents a distributed control strategy based on Fuzzy-Sliding Mode Control (FSMC) for power control of an infrastructure integrated with a DC-Microgrid, which includes photovoltaic, fuel cell and energy storage systems with Plug-in Electric Vehicles (PEVs). In order to implement...... the proposed control strategy, first a general nonlinear modeling of a DC-Microgrid based on related DC-DC converters to each DC power sources is introduced. Secondly, a power management strategy based on fuzzy control for regulating the power flow between the hybrid DC sources, PEVs is proposed. Third...

  13. Speed Synchronization of Multi Induction Motors with Fuzzy Sliding Mode Control

    Directory of Open Access Journals (Sweden)

    HACHEMI Glaoui

    2013-05-01

    Full Text Available A continuous web winding system is a large-scale, complex interconnected dynamic system with numerous tension zones to transport the web while processing it. There are two control schemes for large-scale system control: the centralized scheme and the decentralized scheme. Centralized control is the traditional control method, which considers all the information about the system to be a single dynamic model and design a control system for this model. Aspeed synchronization control strategy for multiple induction motors, based on adjacent cross-coupling control structure, is developed by employing total sliding mode control method. The proposed controlstrategy is to stabilize speed tracking of each induction motor while synchronizing its speed with the speed of the other motors so as to make speed synchronization error amongst induction motors converge to zero. The global stability and the convergence of the designedcontroller are proved by using Lyapunov method. Simulation results demonstrate the effectiveness of the proposed method.

  14. 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.

  15. Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller

    Directory of Open Access Journals (Sweden)

    Jingang Guo

    2014-10-01

    Full Text Available Traditional friction braking torque and motor braking torque can be used in braking for electric vehicles (EVs. A sliding mode controller (SMC based on the exponential reaching law for the anti-lock braking system (ABS is developed to maintain the optimal slip value. Parameter optimizing is applied to the reaching law by fuzzy logic control (FLC. A regenerative braking algorithm, in which the motor torque is taken full advantage of, is adopted to distribute the braking force between the motor braking and the hydraulic braking. Simulations were carried out with Matlab/Simulink. By comparing with a conventional Bang-bang ABS controller, braking stability and passenger comfort is improved with the proposed SMC controller, and the chatting phenomenon is reduced effectively with the parameter optimizing by FLC. With the increasing proportion of the motor braking torque, the tracking of the slip ratio is more rapid and accurate. Furthermore, the braking distance is shortened and the conversion energy is enhanced.

  16. Regulation of Blood Glucose Concentration in Type 1 Diabetics Using Single Order Sliding Mode Control Combined with Fuzzy On-line Tunable Gain, a Simulation Study.

    Science.gov (United States)

    Dinani, Soudabeh Taghian; Zekri, Maryam; Kamali, Marzieh

    2015-01-01

    Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and to alleviate its drawbacks. The aim of the proposed controller that is called SOSMC combined with fuzzy on-line tunable gain is to tune the gain of the controller adaptively. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. As a result, this method can decline the risk of hypoglycemia, a lethal phenomenon in regulating blood glucose level in diabetics caused by a low blood glucose level. Moreover, it attenuates the chattering observed in SOSMC significantly. It is worth noting that in this approach, a mathematical model called minimal model is applied instead of the intravenously infused insulin-blood glucose dynamics. The simulation results demonstrate a good performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. In addition, this method is compared to fuzzy high-order sliding mode control (FHOSMC) and the superiority of the new method compared to FHOSMC is shown in the results.

  17. Symmetric caging formation for convex polygonal object transportation by multiple mobile robots based on fuzzy sliding mode control.

    Science.gov (United States)

    Dai, Yanyan; Kim, YoonGu; Wee, SungGil; Lee, DongHa; Lee, SukGyu

    2016-01-01

    In this paper, the problem of object caging and transporting is considered for multiple mobile robots. With the consideration of minimizing the number of robots and decreasing the rotation of the object, the proper points are calculated and assigned to the multiple mobile robots to allow them to form a symmetric caging formation. The caging formation guarantees that all of the Euclidean distances between any two adjacent robots are smaller than the minimal width of the polygonal object so that the object cannot escape. In order to avoid collision among robots, the parameter of the robots radius is utilized to design the caging formation, and the A⁎ algorithm is used so that mobile robots can move to the proper points. In order to avoid obstacles, the robots and the object are regarded as a rigid body to apply artificial potential field method. The fuzzy sliding mode control method is applied for tracking control of the nonholonomic mobile robots. Finally, the simulation and experimental results show that multiple mobile robots are able to cage and transport the polygonal object to the goal position, avoiding obstacles. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  18. 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

  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. 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. Fuzzy sliding-mode control with low pass filter to reduce chattering ...

    Indian Academy of Sciences (India)

    S Balamurugan

    2017-10-13

    Oct 13, 2017 ... effect: an experimental validation on Quanser SRIP ... Stabilization of a single rotary inverted pendulum (SRIP) system is a benchmark nonlinear control problem. ..... SMC with sign function still exhibits the chattering phe-.

  2. 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.)

  3. 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...

  4. 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.

  5. 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.

  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. 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 ...

  8. 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.

  9. 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.

  10. 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)

  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. 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

  13. 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

  14. 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.

  15. Integrated circuit implementation of fuzzy controllers

    OpenAIRE

    Huertas Díaz, José Luis; Sánchez Solano, Santiago; Baturone Castillo, María Iluminada; Barriga Barros, Ángel

    1996-01-01

    This paper presents mixed-signal current-mode CMOS circuits to implement programmable fuzzy controllers that perform the singleton or zero-order Sugeno’s method. Design equations to characterize these circuits are provided to explain the precision and speed that they offer. This analysis is illustrated with the experimental results of prototypes integrated in standard CMOS technologies. These tests show that an equivalent precision of 6 bits is achieved. The connection of these...

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. 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

  3. 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.

  4. 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)

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. Robust Fuzzy Controllers Using FPGAs

    Science.gov (United States)

    Monroe, Author Gene S., Jr.

    2007-01-01

    Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.

  11. 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.

  12. 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

  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. 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...

  15. 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

  16. 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...

  17. 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

  18. 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.

  19. 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)

  20. 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.

  1. Active structural control with stable fuzzy PID techniques

    CERN Document Server

    Yu, Wen

    2016-01-01

    This book presents a detailed discussion of intelligent techniques to measure the displacement of buildings when they are subjected to vibration. It shows how these techniques are used to control active devices that can reduce vibration 60–80% more effectively than widely used passive anti-seismic systems. After introducing various structural control devices and building-modeling and active structural control methods, the authors propose offset cancellation and high-pass filtering techniques to solve some common problems of building-displacement measurement using accelerometers. The most popular control algorithms in industrial settings, PD/PID controllers, are then analyzed and then combined with fuzzy compensation. The stability of this combination is proven with standard weight-training algorithms. These conditions provide explicit methods for selecting PD/PID controllers. Finally, fuzzy-logic and sliding-mode control are applied to the control of wind-induced vibration. The methods described are support...

  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 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.

  5. 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 ...

  6. 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)

  7. 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.

  8. 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.

  9. 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

  10. 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 ...

  11. 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 ...

  12. 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.

  13. 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.

  14. 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 ...

  15. An Aerial Robot for Rice Farm Quality Inspection With Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm

    DEFF Research Database (Denmark)

    Camci, Efe; Kripalan, Devesh Raju; Ma, Linlu

    2017-01-01

    , an autonomous quality inspection over rice farms is proposed by employing quadcopters. Real-time control of these vehicles, however, is still challenging as they exhibit highly nonlinear behavior especially for agile maneuvers. What is more, these vehicles have to operate under uncertain working conditions...... particle swarm optimization-sliding mode control (PSO-SMC) theory-based hybrid algorithm is proposed for the training of T2-FNNs. In particular, continuous version of PSO is adopted for the identification of the antecedent part of T2-FNNs while SMCbased update rules are utilized for online learning...

  16. 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

  17. 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.

  18. 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.

  19. 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...

  20. 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...

  1. 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.

  2. Dissipativity-Based Reliable Control for Fuzzy Markov Jump Systems With Actuator Faults.

    Science.gov (United States)

    Tao, Jie; Lu, Renquan; Shi, Peng; Su, Hongye; Wu, Zheng-Guang

    2017-09-01

    This paper is concerned with the problem of reliable dissipative control for Takagi-Sugeno fuzzy systems with Markov jumping parameters. Considering the influence of actuator faults, a sufficient condition is developed to ensure that the resultant closed-loop system is stochastically stable and strictly ( Q, S,R )-dissipative based on a relaxed approach in which mode-dependent and fuzzy-basis-dependent Lyapunov functions are employed. Then a reliable dissipative control for fuzzy Markov jump systems is designed, with sufficient condition proposed for the existence of guaranteed stability and dissipativity controller. The effectiveness and potential of the obtained design method is verified by two simulation examples.

  3. 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%.

  4. 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.

  5. 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....

  6. Automatic generation control of TCPS based hydrothermal system under open market scenario: A fuzzy logic approach

    Energy Technology Data Exchange (ETDEWEB)

    Rao, C. Srinivasa [EEE Department, G. Pulla Reddy Engineering College, Kurnool, Andhra Pradesh (India); Nagaraju, S. Siva [EEE Department, J.N.T.U College of Engg., Kakinada, Andhra Pradesh (India); Raju, P. Sangameswara [EEE Department, S.V. University, Tirupati, Andhra Pradesh (India)

    2009-09-15

    This paper presents the analysis of automatic generation control of a two-area interconnected thyristor controlled phase shifter based hydrothermal system in the continuous mode using fuzzy logic controller under open market scenario. Open transmission access and the evolving of more socialized companies for generation, transmission and distribution affects the formulation of AGC problem. So the traditional AGC two-area system is modified to take into account the effect of bilateral contracts on the dynamics. It is possible to stabilize the system frequency and tie-power oscillations by controlling the phase angle of TCPS which is expected to provide a new ancillary service for the future power systems. A control strategy using TCPS is proposed to provide active control of system frequency. Further dynamic responses for small perturbation considering fuzzy logic controller and PI controller (dual mode controller) have been observed and the superior performance of fuzzy logic controller has been reported analytically and also through simulation. (author)

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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 ...

  12. 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)

  13. 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)

  14. 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)

  15. 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

  16. 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.

  17. FUZZY LOGIC CONTROLLER IMPLEMENTATION FOR PHOTOVOLTAIC STATION

    Directory of Open Access Journals (Sweden)

    Imad Zein

    2014-01-01

    Full Text Available Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP, which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. This is why the controllers of all solar power electronic converters employ some method for maximum power point tracking (MPPT . Over the past years many MPPT techniques have been published and based on that the main paper’s objective is to analyze one of the most promising MPPT control algorithms: fuzzy logic controller

  18. Feedback Linearization Control of a Shunt Active Power Filter Using a Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Tianhua Li

    2013-09-01

    Full Text Available In this paper, a novel feedback linearization based sliding mode controlled parallel active power filter using a fuzzy controller is presented in a three-phase three-wire grid. A feedback linearization control with fuzzy parameter self-tuning is used to implement the DC side voltage regulation while a novel integral sliding mode controller is applied to reduce the total harmonic distortion of the supply current. Since traditional unit synchronous sinusoidal signal calculation methods are not applicable when the supply voltage contains harmonics, a novel unit synchronous sinusoidal signal computing method based on synchronous frame transforming theory is presented to overcome this disadvantage. The simulation results verify that the DC side voltage is very stable for the given value and responds quickly to the external disturbance. A comparison is also made to show the advantages of the novel unit sinusoidal signal calculating method and the super harmonic treatment property of the designed active power filter.

  19. fuzzy control technique fuzzy control technique applied to modified

    African Journals Online (AJOL)

    eobe

    epidemiological parameters) to the malaria model simulated by 9 fully ... The Mamdani controllers use a standard max-min inference process and a fast centre of min inference process and a ... Numerical results obtained using Matlab 2008a software software .... simulation environment using the 9 ODE Simulators. The test ...

  20. Failure mode and effects analysis A fuzzy group MCDM approach

    Directory of Open Access Journals (Sweden)

    A. Hadi-Vencheh

    2013-08-01

    Full Text Available In this paper, a new fuzzy group decision making (FGDM model based on alpha-level sets, is proposed to generate, more accurate fuzzy using, risk priority numbers (RPNs and ensure to be robust against the uncertainty. This model allows decision makers (DMs to evaluate FMEA risk factors using linguistic terms rather than precise numerical values, allows them to express their opinions independently. A case study is investigated using the proposed model to illustrate its applications in RPN assessment.

  1. 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...

  2. Project Delivery System Mode Decision Based on Uncertain AHP and Fuzzy Sets

    Science.gov (United States)

    Kaishan, Liu; Huimin, Li

    2017-12-01

    The project delivery system mode determines the contract pricing type, project management mode and the risk allocation among all participants. Different project delivery system modes have different characteristics and applicable scope. For the owners, the selection of the delivery mode is the key point to decide whether the project can achieve the expected benefits, it relates to the success or failure of project construction. Under the precondition of comprehensively considering the influence factors of the delivery mode, the model of project delivery system mode decision was set up on the basis of uncertain AHP and fuzzy sets, which can well consider the uncertainty and fuzziness when conducting the index evaluation and weight confirmation, so as to rapidly and effectively identify the most suitable delivery mode according to project characteristics. The effectiveness of the model has been verified via the actual case analysis in order to provide reference for the construction project delivery system mode.

  3. 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 ...

  4. 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.

  5. 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.

  6. 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.

  7. 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 ...

  8. Sliding mode control and observation

    CERN Document Server

    Shtessel, Yuri; Fridman, Leonid; Levant, Arie

    2014-01-01

    The sliding mode control methodology has proven effective in dealing with complex dynamical systems affected by disturbances, uncertainties and unmodeled dynamics. Robust control technology based on this methodology has been applied to many real-world problems, especially in the areas of aerospace control, electric power systems, electromechanical systems, and robotics. Sliding Mode Control and Observation represents the first textbook that starts with classical sliding mode control techniques and progresses toward newly developed higher-order sliding mode control and observation algorithms and their applications. The present volume addresses a range of sliding mode control issues, including: *Conventional sliding mode controller and observer design *Second-order sliding mode controllers and differentiators *Frequency domain analysis of conventional and second-order sliding mode controllers *Higher-order sliding mode controllers and differentiators *Higher-order sliding mode observers *Sliding mode disturbanc...

  9. 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.

  10. Fuzzy logic-based battery charge controller

    International Nuclear Information System (INIS)

    Daoud, A.; Midoun, A.

    2006-01-01

    Photovoltaic power system are generally classified according to their functional and operational requirements, their component configurations, and how the equipment is connected to other power sources and electrical loads, photovoltaic systems can be designed to provide DC and/or AC power service, can operate interconnected with or independent of the utility grid, and can be connected with other energy sources and energy storage systems. Batteries are often used in PV systems for the purpose of storing energy produced by the PV array during the day, and to supply it to electrical loads as needed (during the night and periods of cloudy weather). The lead acid battery, although know for more than one hundred years, has currently offered the best response in terms of price, energetic efficiency and lifetime. The main function of controller or regulator in PV system is too fully charge the battery without permitting overcharge while preventing reverse current flow at night. If a no-self-regulating solar array is connected to lead acid batteries with no overcharge protection, battery life will be compromised. Simple controllers contain a transistor that disconnects or reconnects the PV in the charging circuit once a pre-set voltage is reached. More sophisticated controllers utilize pulse with modulation (PWM) to assure the battery is being fully charged. The first 70% to 80% of battery capacity is easily replaced, but the last 20% to 30% requires more attention and therefore more complexity. This complexity is avoided by using a skilled operators experience in the form of the rules. Thus a fuzzy control system seeks to control the battery that cannot be controlled well by a conventional control such as PID, PD, PI etc., due to the unavailability of an accurate mathematical model of the battery. In this paper design of an intelligent battery charger, in which the control algorithm is implemented with fuzzy logic is discussed. The digital architecture is implemented with

  11. 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

  12. 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)

  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 logic control of vehicle suspensions with dry friction nonlinearity

    Indian Academy of Sciences (India)

    Fuzzy logic control; active vehicle suspension; suspension space. 1. ... surface unevenness, stability and directional control during handling ..... Burton A W, Truscott A J, Wellstead P E 1995 Analysis, modeling and control of an advanced.

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

    Directory of Open Access Journals (Sweden)

    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.

  16. 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...

  17. 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).

  18. 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.

  19. 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.

  20. 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, ...

  1. System control fuzzy neural sewage pumping stations using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Владлен Николаевич Кузнецов

    2015-06-01

    Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

  2. 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 ...

  3. 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.

  4. 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

  5. Distributed traffic signal control using fuzzy logic

    Science.gov (United States)

    Chiu, Stephen

    1992-01-01

    We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.

  6. 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

  7. Tracking control of DC motors via mimo nonlinear fuzzy control

    International Nuclear Information System (INIS)

    Harb, Ahmad M.; Smadi, Issam A.

    2009-01-01

    This paper proposed a nonlinear controller for speed tracking of separately excited DC motors (SEDCM's) using the multi-input multi-output (MIMO) fuzzy logic controller (FLC's). Based on a nonlinear mathematical model of SEDCM, a FLC is designed to achieve high performance speed tracking through rejection load disturbance. Computer simulations are presented to show speed tracking performance and the effectiveness of the proposed controller.

  8. 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

  9. Efficient Fuzzy Logic Controller for Magnetic Levitation Systems

    African Journals Online (AJOL)

    Akorede

    ABSTRACT: Magnetic levitation is a system of suspending a body or a complete system against gravity. Suspending a system ... disturbance signal was applied to the input of the control system. Fuzzy ..... Automatic Control System, fifth edition.

  10. 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

  11. 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.

  12. 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)

  13. 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.

  14. 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.

  15. Chemical optimization algorithm for fuzzy controller design

    CERN Document Server

    Astudillo, Leslie; Castillo, Oscar

    2014-01-01

    In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application

  16. 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.

  17. FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK

    Directory of Open Access Journals (Sweden)

    Katsuhiro Honda

    2017-01-01

    Full Text Available Cocluster structure analysis is a basic technique for revealing intrinsic structural information from cooccurrence data among objects and items, in which coclusters are composed of mutually familiar pairs of objects and items. In many real applications, it is also the case that we have not only cooccurrence information among objects and items but also intrinsic relation among items and other ingredients. For example, in food preference analysis, users’ preferences on foods should be found considering not only user-food cooccurrences but also the implicit relation among users and cooking ingredients. In this paper, two FCM-type fuzzy coclustering models, that is, FCCM and Fuzzy CoDoK, are extended for revealing intrinsic cocluster structures from three-mode cooccurrence data, where the aggregation degree of three elements in each cocluster is maximized through iterative updating of three types of fuzzy memberships for objects, items, and ingredients. The characteristic features of the proposed methods are demonstrated through a numerical experiment.

  18. Maximum power point tracking algorithm based on sliding mode and fuzzy logic for photovoltaic sources under variable environmental conditions

    Science.gov (United States)

    Atik, L.; Petit, P.; Sawicki, J. P.; Ternifi, Z. T.; Bachir, G.; Della, M.; Aillerie, M.

    2017-02-01

    Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP), which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. Various methods for maximum power point tracking (MPPT) were developed and finally implemented in solar power electronic controllers to increase the efficiency in the electricity production originate from renewables. In this paper we compare using Matlab tools Simulink, two different MPP tracking methods, which are, fuzzy logic control (FL) and sliding mode control (SMC), considering their efficiency in solar energy production.

  19. 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

  20. 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

  1. 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)

  2. 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

  3. 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.

  4. 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).

  5. 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.

  6. 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.

  7. 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....

  8. Robust hydraulic position controller by a fuzzy state controller

    International Nuclear Information System (INIS)

    Zhao, T.; Van der Wal, A.J.

    1994-01-01

    In nuclear industry, one of the most important design considerations of controllers is their robustness. Robustness in this context is defined as the ability of a system to be controlled in a stable way over a wide range of system parameters. Generally the systems to be controlled are linearized, and stability is subsequently proven for this idealized system. By combining classical control theory and fuzzy set theory, a new kind of state controller is proposed and successfully applied to a hydraulic position servo with excellent robustness against variation of system parameters

  9. Switching Fuzzy Guaranteed Cost Control for Nonlinear Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Linqin Cai

    2014-01-01

    Full Text Available This paper deals with the problem of guaranteed cost control for a class of nonlinear networked control systems (NCSs with time-varying delay. A guaranteed cost controller design method is proposed to achieve the desired control performance based on the switched T-S fuzzy model. The switching mechanism is introduced to handle the uncertainties of NCSs. Based on Lyapunov functional approach, some sufficient conditions for the existence of state feedback robust guaranteed cost controller are presented. Simulation results show that the proposed method is effective to guarantee system’s global asymptotic stability and quality of service (QoS.

  10. 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.

  11. DYNAMIC SIMULATION AND FUZZY CONTROL OF A CONTINUOUS DISTILLATION COLUMN

    OpenAIRE

    Arbildo López, A.; Lombira Echevarría, J.; Osario López, l.

    2014-01-01

    The objective of this work is the study of the dinamic simulation and fuzzy control of a multicomponent continuous distillation column. In this work, the mathematical model of the distillation column and the computing program for the simulation are described. Also, the structure and implementation of the fuzzy controller are presentad. Finally, the results obtained using this programare compared with those reported in the scientific literature for different mixtures. El objetivo de nuestra...

  12. 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...

  13. 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).

  14. 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.

  15. 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.

  16. A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2015-01-01

    Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.

  17. Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis

    International Nuclear Information System (INIS)

    Bowles, John B.; Pelaez, C.E.

    1995-01-01

    This paper describes a new technique, based on fuzzy logic, for prioritizing failures for corrective actions in a Failure Mode, Effects and Criticality Analysis (FMECA). As in a traditional criticality analysis, the assessment is based on the severity, frequency of occurrence, and detectability of an item failure. However, these parameters are here represented as members of a fuzzy set, combined by matching them against rules in a rule base, evaluated with min-max inferencing, and then defuzzified to assess the riskiness of the failure. This approach resolves some of the problems in traditional methods of evaluation and it has several advantages compared to strictly numerical methods: 1) it allows the analyst to evaluate the risk associated with item failure modes directly using the linguistic terms that are employed in making the criticality assessment; 2) ambiguous, qualitative, or imprecise information, as well as quantitative data, can be used in the assessment and they are handled in a consistent manner; and 3) it gives a more flexible structure for combining the severity, occurrence, and detectability parameters. Two fuzzy logic based approaches for assessing criticality are presented. The first is based on the numerical rankings used in a conventional Risk Priority Number (RPN) calculation and uses crisp inputs gathered from the user or extracted from a reliability analysis. The second, which can be used early in the design process when less detailed information is available, allows fuzzy inputs and also illustrates the direct use of the linguistic rankings defined for the RPN calculations

  18. 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

  19. 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.

  20. Research on frequency control strategy of interconnected region based on fuzzy PID

    Science.gov (United States)

    Zhang, Yan; Li, Chunlan

    2018-05-01

    In order to improve the frequency control performance of the interconnected power grid, overcome the problems of poor robustness and slow adjustment of traditional regulation, the paper puts forward a frequency control method based on fuzzy PID. The method takes the frequency deviation and tie-line deviation of each area as the control objective, takes the regional frequency deviation and its deviation as input, and uses fuzzy mathematics theory, adjusts PID control parameters online. By establishing the regional frequency control model of water-fire complementary power generation in MATLAB, the regional frequency control strategy is given, and three control modes (TBC-FTC, FTC-FTC, FFC-FTC) are simulated and analyzed. The simulation and experimental results show that, this method has better control performance compared with the traditional regional frequency regulation.

  1. Hybridizing fuzzy control and timed automata for modeling variable structure fuzzy systems

    NARCIS (Netherlands)

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

    2010-01-01

    During the past several years, fuzzy control has emerged as one of the most suitable and efficient methods for designing and developing complex systems in environments characterized by high level of uncertainty and imprecision. Nowadays, this methodology is used to model systems in several

  2. 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.

  3. 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

  4. 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.

  5. 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)

  6. 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.

  7. The comparison of manual and LabVIEW-based fuzzy control on mechanical ventilation.

    Science.gov (United States)

    Guler, Hasan; Ata, Fikret

    2014-09-01

    The aim of this article is to develop a knowledge-based therapy for management of rats with respiratory distress. A mechanical ventilator was designed to achieve this aim. The designed ventilator is called an intelligent mechanical ventilator since fuzzy logic was used to control the pneumatic equipment according to the rat's status. LabVIEW software was used to control all equipments in the ventilator prototype and to monitor respiratory variables in the experiment. The designed ventilator can be controlled both manually and by fuzzy logic. Eight female Wistar-Albino rats were used to test the designed ventilator and to show the effectiveness of fuzzy control over manual control on pressure control ventilation mode. The anesthetized rats were first ventilated for 20 min manually. After that time, they were ventilated for 20 min by fuzzy logic. Student's t-test for p < 0.05 was applied to the measured minimum, maximum and mean peak inspiration pressures to analyze the obtained results. The results show that there is no statistical difference in the rat's lung parameters before and after the experiments. It can be said that the designed ventilator and developed knowledge-based therapy support artificial respiration of living things successfully. © IMechE 2014.

  8. 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.

  9. Hybrid Engine Powered City Car: Fuzzy Controlled Approach

    Science.gov (United States)

    Rahman, Ataur; Mohiuddin, AKM; Hawlader, MNA; Ihsan, Sany

    2017-03-01

    This study describes a fuzzy controlled hybrid engine powered car. The car is powered by the lithium ion battery capacity of 1000 Wh is charged by the 50 cc hybrid engine and power regenerative mode. The engine is operated with lean mixture at 3000 rpm to charge the battery. The regenerative mode that connects with the engine generates electrical power of 500-600 W for the deceleration of car from 90 km/h to 20 km/h. The regenerated electrical power has been used to power the air-conditioning system and to meet the other electrical power. The battery power only used to propel the car. The regenerative power also found charging the battery for longer operation about 40 minutes and more. The design flexibility of this vehicle starts with whole-vehicle integration based on radical light weighting, drag reduction, and accessory efficiency. The energy efficient hybrid engine cut carbon dioxide (CO2) and nitrogen oxides (N2O) emission about 70-80% as the loads on the crankshaft such as cam-follower and its associated rotating components are replaced by electromagnetic systems, and the flywheel, alternator and starter motor are replaced by a motor generator. The vehicle was tested and found that it was able to travel 70 km/litre with the power of hybrid engine.

  10. 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.

  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. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Fuzzy logic control of steam generator water level in pressurized water reactors

    International Nuclear Information System (INIS)

    Kuan, C.C.; Lin, C.; Hsu, C.C.

    1992-01-01

    In this paper a fuzzy logic controller is applied to control the steam generator water level in a pressurized water reactor. The method does not require a detailed mathematical mode of the object to be controlled. The design is based on a set of linguistic rules that were adopted from the human operator's experience. After off-line fuzzy computation, the controller is a lookup table, and thus, real-time control is achieved. Shrink-and-swell phenomena are considered in the linguistic rules, and the simulation results show that their effect is dramatically reduced. The performance of the control system can also be improved by changing the input and output scaling factors, which is convenient for on-line tuning

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. Control of a classical microtron and application of fuzzy logic

    International Nuclear Information System (INIS)

    Krist, Pavel; Bila, Jiri

    2011-01-01

    Control problems of the classical microtron with a Kapitza type accelerating cavity were addressed. A fuzzy controller was used, which enabled the system to be controlled even though the accelerating voltage, whose setting is vital for maintaining the accelerator in the stable state, cannot not be measured

  2. 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.

  3. 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.

  4. Fuzzy Universal Model Approximator for Distributed Solar Collector Field Control

    KAUST Repository

    Elmetennani, Shahrazed

    2014-07-01

    This paper deals with the control of concentrating parabolic solar collectors by forcing the outlet oil temperature to track a set reference. A fuzzy universal approximate model is introduced in order to accurately reproduce the behavior of the system dynamics. The proposed model is a low order state space representation derived from the partial differential equation describing the oil temperature evolution using fuzzy transform theory. The resulting set of ordinary differential equations simplifies the system analysis and the control law design and is suitable for real time control implementation. Simulation results show good performance of the proposed model.

  5. Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system

    Directory of Open Access Journals (Sweden)

    Houman HANACHI

    2018-01-01

    Full Text Available Gas Turbine Engines (GTEs are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gas-path components of a GTE are exposed to harsh operating and ambient conditions, leading to several degradation mechanisms. Because GTE components are mostly inaccessible for direct measurements and their degradation levels must be inferred from the measurements of accessible parameters, it is a challenge to acquire reliable information on the degradation conditions of the parts in different fault modes. In this work, a data-driven fault detection and degradation estimation scheme is developed for GTE diagnostics based on an Adaptive Neuro-Fuzzy Inference System (ANFIS. To verify the performance and accuracy of the developed diagnostic framework on GTE data, an ensemble of measurable gas path parameters has been generated by a high-fidelity GTE model under (a diverse ambient conditions and control settings, (b every possible combination of degradation symptoms, and (c a broad range of signal to noise ratios. The results prove the competency of the developed framework in fault diagnostics and reveal the sensitivity of diagnostic results to measurement noise for different degradation symptoms.

  6. 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

  7. 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.

  8. 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

  9. 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

  10. Free chattering hybrid sliding mode control for a class of non-linear systems

    DEFF Research Database (Denmark)

    Khooban, Mohammad Hassan; Niknam, Taher; Blaabjerg, Frede

    2016-01-01

    In current study, in order to find the control of general uncertain nonlinear systems, a new optimal hybrid control approach called Optimal General Type II Fuzzy Sliding Mode (OGT2FSM) is presented. In order to estimate unknown nonlinear activities in monitoring dynamic uncertainties, the benefits...... on the same topic, which are an Adaptive Interval Type-2 Fuzzy Logic Controller (AGT2FLC) and Conventional Sliding Mode Controller (CSMC), to assess the efficiency of the suggested controller. The suggested control scheme is finally used to the Electric Vehicles type as a case study. Results of simulation...

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. Pneumatic motor speed control by trajectory tracking fuzzy logic

    Indian Academy of Sciences (India)

    In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is defined to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to find the TTFLC boundary values of membership functions ...

  16. Self-learning fuzzy logic controllers based on reinforcement

    International Nuclear Information System (INIS)

    Wang, Z.; Shao, S.; Ding, J.

    1996-01-01

    This paper proposes a new method for learning and tuning Fuzzy Logic Controllers. The self-learning scheme in this paper is composed of Bucket-Brigade and Genetic Algorithm. The proposed method is tested on the cart-pole system. Simulation results show that our approach has good learning and control performance

  17. 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.

  18. Fuzzy logic controller to improve powerline communication

    Science.gov (United States)

    Tirrito, Salvatore

    2015-12-01

    The Power Line Communications (PLC) technology allows the use of the power grid in order to ensure the exchange of data information among devices. This work proposes an approach, based on Fuzzy Logic, that dynamically manages the amplitude of the signal, with which each node transmits, by processing the master-slave link quality measured and the master-slave distance. The main objective of this is to reduce both the impact of communication interferences induced and power consumption.

  19. A fuzzy behaviorist approach to sensor-based robot control

    Energy Technology Data Exchange (ETDEWEB)

    Pin, F.G.

    1996-05-01

    Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-based approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.

  20. 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. .

  1. 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.

  2. Fuzzy logic control for selective hydrogenation of acetylene in ethylene rich streams using visual basic

    International Nuclear Information System (INIS)

    Malik, S.R.; Suleman, H.; Khan, J.R.

    2010-01-01

    Presence of acetylene is technically disadvantageous in the ethylene rich gas streams from steam crackers. Acetylene tends to polymerize and inactivates the transition metal catalysts, forming highly explosive compounds. The acetylene content has to be selectively reduced to less than one part per million for such streams. The acetylene hydrogenation unit requires stringent control parameters and needs specialized process control techniques for its operation. This study is concerned with application of Fuzzy Logic Control to manipulate and control the process plant with higher precision and greater simplicity. The control program has been written in visual Basic and entails all major scenarios of work modes for successful hydrogenation of Acetylene. (author)

  3. Neutral network and fuzzy logic based grate control; Roststyrning med neutrala naetverk och fuzzy logic

    Energy Technology Data Exchange (ETDEWEB)

    Ramstroem, Erik [TPS Termiska Processer AB, Nykoeping (Sweden)

    2002-04-01

    Grate-control is a complex task in many ways. The relations between controlled variables and the values they depend on are mostly unknown. Research projects are going on to create grate models based on physical laws. Those models are too complex for control implementation. The evaluation time is to long for control use. Another fundamental difficulty is that the relationships are none linear. That is, for a specific change in control value, the change in controlled value depends on the original size of control value, process disturbances and controlled values. There are extensive theories for linear process control. Non-linear control theory is used in robotic applications, but not in process and combustion control. The aim of grate control is to use as much of the grate area as possible, without having unburned material in ash. The outlined strategy is: To keep the position of the final bum out zone constant and its extension controlled. The control variables should be primary airflow, distribution of primary air, and fuel flow. Disturbances that should be measured are the fuel moisture content, the temperature of primary air and the grate temperature under the fuel bed. Technologies used are, fuzzy-logic and neural networks. A combination of booth could be used as well as any of them separately. A Fuzzy-logic controller acts as a computerised operator. Rules are specified with 'if - then' thesis. An example of that is: - if temperature is low, then close the valve The boundaries between the rules are made fuzzy. That makes it possible for the temperature to be just a bit low, which makes the valve open a bit. A lot of rules are created so that the controller knows what to do in every situation. Neural networks are sort of multi dimensional curves, with arbitrary degrees of freedom. The nets are used to predict future process values from measured ones. The model is evaluated from collected data. Parameters are adjusted for best correspondence between

  4. 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.)

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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)

  10. 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...

  11. 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....

  12. Fuzzy logic control of vehicle suspensions with dry friction nonlinearity

    Indian Academy of Sciences (India)

    We design and investigate the performance of fuzzy logic-controlled (FLC) active suspensions on a nonlinear vehicle model with four degrees of freedom, without causing any degeneration in suspension working limits. Force actuators were mounted parallel to the suspensions. In this new approach, linear combinations of ...

  13. Fuzzy controller for better tennis ball robot | Nguyen | Journal of ...

    African Journals Online (AJOL)

    This paper aims at designing a tennis ball robot as a training facility for tennis players. The robot is built with fuzzy controller which provides proper techniques for the players to gain practical experience as well as technical skills; thus, it can effectively serve the community and train athletes in the high-performance sport.

  14. Systems control with generalized probabilistic fuzzy-reinforcement learning

    NARCIS (Netherlands)

    Hinojosa, J.; Nefti, S.; Kaymak, U.

    2011-01-01

    Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be

  15. Fuzzy Logic Applied to an Oven Temperature Control System

    Directory of Open Access Journals (Sweden)

    Nagabhushana KATTE

    2011-10-01

    Full Text Available The paper describes the methodology of design and development of fuzzy logic based oven temperature control system. As simple fuzzy logic controller (FLC structure with an efficient realization and a small rule base that can be easily implemented in existing underwater control systems is proposed. The FLC has been designed using bell-shaped membership function for fuzzification, 49 control rules in its rule base and centre of gravity technique for defuzzification. Analog interface card with 16-bits resolution is designed to achieve higher precision in temperature measurement and control. The experimental results of PID and FLC implemented system are drawn for a step input and presented in a comparative fashion. FLC exhibits fast response and it has got sharp rise time and smooth control over conventional PID controller. The paper scrupulously discusses the hardware and software (developed using ‘C’ language features of the system.

  16. 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.

  17. 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.

  18. 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

  19. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Li [Tampere Univ. of Technology (Finland); Eriksson, J T [Tampere Univ. of Technology (Finland)

    1996-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  20. Research on laser cladding control system based on fuzzy PID

    Science.gov (United States)

    Zhang, Chuanwei; Yu, Zhengyang

    2017-12-01

    Laser cladding technology has a high demand for control system, and the domestic laser cladding control system mostly uses the traditional PID control algorithm. Therefore, the laser cladding control system has a lot of room for improvement. This feature is suitable for laser cladding technology, Based on fuzzy PID three closed-loop control system, and compared with the conventional PID; At the same time, the laser cladding experiment and friction and wear experiment were carried out under the premise of ensuring the reasonable control system. Experiments show that compared with the conventional PID algorithm in fuzzy the PID algorithm under the surface of the cladding layer is more smooth, the surface roughness increases, and the wear resistance of the cladding layer is also enhanced.

  1. A neuro-fuzzy controlling algorithm for wind turbine

    Energy Technology Data Exchange (ETDEWEB)

    Li Lin [Tampere Univ. of Technology (Finland); Eriksson, J.T. [Tampere Univ. of Technology (Finland)

    1995-12-31

    The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

  2. Implementation Of Fuzzy Automated Brake Controller Using TSK Algorithm

    Science.gov (United States)

    Mittal, Ruchi; Kaur, Magandeep

    2010-11-01

    In this paper an application of Fuzzy Logic for Automatic Braking system is proposed. Anti-blocking system (ABS) brake controllers pose unique challenges to the designer: a) For optimal performance, the controller must operate at an unstable equilibrium point, b) Depending on road conditions, the maximum braking torque may vary over a wide range, c) The tire slippage measurement signal, crucial for controller performance, is both highly uncertain and noisy. A digital controller design was chosen which combines a fuzzy logic element and a decision logic network. The controller identifies the current road condition and generates a command braking pressure signal Depending upon the speed and distance of train. This paper describes design criteria, and the decision and rule structure of the control system. The simulation results present the system's performance depending upon the varying speed and distance of the train.

  3. 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

  4. 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.

  5. 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.

  6. Burst mode control

    DEFF Research Database (Denmark)

    2015-01-01

    to hysteresis on/off control, the new method does not require immediate responses to comparisons of a sense voltage to two reference voltages; rather, according to the new method, only one reference voltage is used, and delays in the feedback loop are allowed to cause some variation of an output of the power...

  7. 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

  8. Switch Reluctance Motor Control Based on Fuzzy Logic System

    Directory of Open Access Journals (Sweden)

    S. V. Aleksandrovsky

    2012-01-01

    Full Text Available Due to its intrinsic simplicity and reliability, the switched reluctance motor (SRM has now become a promising candidate for variable-speed drive applications as an alternative induction motor in various industrial application. However, the SRM has the disadvantage of nonlinear characteristic and control. It is suggested to use controller based on fuzzy logic system. Design of FLS controller and simulation model presented.

  9. 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

  10. 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.

  11. 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.

  12. 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)

  13. 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

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

    Directory of Open Access Journals (Sweden)

    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.

  15. Study of Inverted Pendulum Robot Using Fuzzy Servo Control Method

    Directory of Open Access Journals (Sweden)

    Dazhong Wang

    2012-09-01

    Full Text Available The inverted pendulum robot is a classical problem in controls. The inherit instabilities in the setup make it a natural target for a control system. Inverted pendulum robot is suitable to use for investigation and verification of various control methods for dynamic systems. Maintaining an equilibrium position of the pendulum pointing up is a challenge as this equilibrium position is unstable. As the inverted pendulum robot system is nonlinear it is well-suited to be controlled by fuzzy logic. In this paper, Lagrange method has been applied to develop the mathematical model of the system. The objective of the simulation to be shown using the fuzzy control method can stabilize the nonlinear system of inverted pendulum robot.

  16. 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.

  17. 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)

  18. 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.

  19. 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.)

  20. 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...

  1. 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

  2. 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

  3. A fuzzy logic approach to control anaerobic digestion.

    Science.gov (United States)

    Domnanovich, A M; Strik, D P; Zani, L; Pfeiffer, B; Karlovits, M; Braun, R; Holubar, P

    2003-01-01

    One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithms.

  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. Expected value based fuzzy programming approach to solve integrated supplier selection and inventory control problem with fuzzy demand

    Science.gov (United States)

    Sutrisno; Widowati; Sunarsih; Kartono

    2018-01-01

    In this paper, a mathematical model in quadratic programming with fuzzy parameter is proposed to determine the optimal strategy for integrated inventory control and supplier selection problem with fuzzy demand. To solve the corresponding optimization problem, we use the expected value based fuzzy programming. Numerical examples are performed to evaluate the model. From the results, the optimal amount of each product that have to be purchased from each supplier for each time period and the optimal amount of each product that have to be stored in the inventory for each time period were determined with minimum total cost and the inventory level was sufficiently closed to the reference level.

  6. 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.)

  7. Feedforward Tracking Control of Flat Recurrent Fuzzy Systems

    International Nuclear Information System (INIS)

    Gering, Stefan; Adamy, Jürgen

    2014-01-01

    Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis

  8. Feedforward Tracking Control of Flat Recurrent Fuzzy Systems

    Science.gov (United States)

    Gering, Stefan; Adamy, Jürgen

    2014-12-01

    Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis.

  9. Fuzzy Control of Tidal volume, Respiration number and Pressure value

    OpenAIRE

    Hasan Guler; Fikret Ata

    2010-01-01

    In this study, control of tidal volume, respiration number and pressure value which are arrived to patient at mechanical ventilator device which is used in intensive care units were performed with fuzzy logic controller. The aim of this system is to reduce workload of aneshesiologist. By calculating tidal volume, respiration number and pressure value, the error Pe(k) between reference pressure value (Pref) and pressure of gas given ill person (Phasta) and error change rate ;#948;Pe(k) were co...

  10. The integration methods of fuzzy fault mode and effect analysis and fault tree analysis for risk analysis of yogurt production

    Science.gov (United States)

    Aprilia, Ayu Rizky; Santoso, Imam; Ekasari, Dhita Murita

    2017-05-01

    Yogurt is a product based on milk, which has beneficial effects for health. The process for the production of yogurt is very susceptible to failure because it involves bacteria and fermentation. For an industry, the risks may cause harm and have a negative impact. In order for a product to be successful and profitable, it requires the analysis of risks that may occur during the production process. Risk analysis can identify the risks in detail and prevent as well as determine its handling, so that the risks can be minimized. Therefore, this study will analyze the risks of the production process with a case study in CV.XYZ. The method used in this research is the Fuzzy Failure Mode and Effect Analysis (fuzzy FMEA) and Fault Tree Analysis (FTA). The results showed that there are 6 risks from equipment variables, raw material variables, and process variables. Those risks include the critical risk, which is the risk of a lack of an aseptic process, more specifically if starter yogurt is damaged due to contamination by fungus or other bacteria and a lack of sanitation equipment. The results of quantitative analysis of FTA showed that the highest probability is the probability of the lack of an aseptic process, with a risk of 3.902%. The recommendations for improvement include establishing SOPs (Standard Operating Procedures), which include the process, workers, and environment, controlling the starter of yogurt and improving the production planning and sanitation equipment using hot water immersion.

  11. Modeling and control of an unstable system using probabilistic fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Sozhamadevi N.

    2015-09-01

    Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

  12. 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...

  13. Fuzzy controller for real time supervision of nuclear power reactor

    International Nuclear Information System (INIS)

    Bala Subramanian, R.

    2012-01-01

    Generally nuclear energy provides about 60% of the whole electricity production. A modulation of the nuclear power plants must be able to respond to the demand on the network. The pressurized water nuclear reactor has to yield correctly a load set point. Fundamentally, two parameters are concerned in leading this task to a successful conclusion: the power axial-offset and the control rods position. The focus of this study is the automation of the control of the power axial-offset by adding soluble boron and by minimizing the volume flows through the water pump. It is also important to take into consideration the liquid waste volume. Water or boron is injected into the reactor primary circuit. At the present time this task is still performed manually by an operator, for all previous attempts to automate it failed. That device, sketchily described in the paper, gave rise to the development of a real-time fuzzy controller for the power axial-offset and the control rods insertion in a pressurized water reactor (PWR). The fuzzy controller, which is the main subject of the paper, expresses more naturally the human expertise, thus avoiding the previous issue of empirical tunings. It was implemented in simulation using Matlab-Simulink on a Sun workstation. Two realistic tests discussed show that the fuzzy controller runs as efficiently as an expert operator does

  14. 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

  15. 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

  16. Navigation Algorithm Using Fuzzy Control Method in Mobile Robotics

    Directory of Open Access Journals (Sweden)

    Cviklovič Vladimír

    2016-03-01

    Full Text Available The issue of navigation methods is being continuously developed globally. The aim of this article is to test the fuzzy control algorithm for track finding in mobile robotics. The concept of an autonomous mobile robot EN20 has been designed to test its behaviour. The odometry navigation method was used. The benefits of fuzzy control are in the evidence of mobile robot’s behaviour. These benefits are obtained when more physical variables on the base of more input variables are controlled at the same time. In our case, there are two input variables - heading angle and distance, and two output variables - the angular velocity of the left and right wheel. The autonomous mobile robot is moving with human logic.

  17. Flocking of quad-rotor UAVs with fuzzy control.

    Science.gov (United States)

    Mao, Xiang; Zhang, Hongbin; Wang, Yanhui

    2018-03-01

    This paper investigates the flocking problem of quad-rotor UAVs. Considering the actual situations, we derived a new simplified quad-rotor UAV model which is more reasonable. Based on the model, the T-S fuzzy model of attitude dynamic equation and the corresponding T-S fuzzy feedback controller are discussed. By introducing a double-loop control construction, we adjust its attitude to realize the position control. Then a flocking algorithm is proposed to achieve the flocking of the quad-rotor UAVs. Compared with the flocking algorithm of the mass point model, we dealt with the collision problem of the quad-rotor UAVs. In order to improve the airspace utilization, a more compact configuration called quasi e-lattice is constructed to guarantee the compact flight of the quad-rotor UAVs. Finally, numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Fuzzy Logic Supervised Teleoperation Control for Mobile Robot

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The supervised teleoperation control is presented for a mobile robot to implement the tasks by using fuzzy logic. The teleoperation control system includes joystick based user interaction mechanism, the high level instruction set and fuzzy logic behaviors integrated in a supervised autonomy teleoperation control system for indoor navigation. These behaviors include left wall following, right wall following, turn left, turn right, left obstacle avoidance, right obstacle avoidance and corridor following based on ultrasonic range finders data. The robot compares the instructive high level command from the operator and relays back a suggestive signal back to the operator in case of mismatch between environment and instructive command. This strategy relieves the operator's cognitive burden, handle unforeseen situations and uncertainties of environment autonomously. The effectiveness of the proposed method for navigation in an unstructured environment is verified by experiments conducted on a mobile robot equipped with only ultrasonic range finders for environment sensing.

  19. Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method.

    Science.gov (United States)

    Deng, Xinyang; Jiang, Wen

    2017-09-12

    Failure mode and effect analysis (FMEA) is a useful tool to define, identify, and eliminate potential failures or errors so as to improve the reliability of systems, designs, and products. Risk evaluation is an important issue in FMEA to determine the risk priorities of failure modes. There are some shortcomings in the traditional risk priority number (RPN) approach for risk evaluation in FMEA, and fuzzy risk evaluation has become an important research direction that attracts increasing attention. In this paper, the fuzzy risk evaluation in FMEA is studied from a perspective of multi-sensor information fusion. By considering the non-exclusiveness between the evaluations of fuzzy linguistic variables to failure modes, a novel model called D numbers is used to model the non-exclusive fuzzy evaluations. A D numbers based multi-sensor information fusion method is proposed to establish a new model for fuzzy risk evaluation in FMEA. An illustrative example is provided and examined using the proposed model and other existing method to show the effectiveness of the proposed model.

  20. 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.

  1. Design of a Polynomial Fuzzy Observer Controller With Sampled-Output Measurements for Nonlinear Systems Considering Unmeasurable Premise Variables

    OpenAIRE

    Liu, Chuang; Lam, H. K.

    2015-01-01

    In this paper, we propose a polynomial fuzzy observer controller for nonlinear systems, where the design is achieved through the stability analysis of polynomial-fuzzy-model-based (PFMB) observer-control system. The polynomial fuzzy observer estimates the system states using estimated premise variables. The estimated states are then employed by the polynomial fuzzy controller for the feedback control of nonlinear systems represented by the polynomial fuzzy model. The system stability of the P...

  2. 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.

  3. Nonlinear Predictive Sliding Mode Control for Active Suspension System

    Directory of Open Access Journals (Sweden)

    Dazhuang Wang

    2018-01-01

    Full Text Available An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.

  4. 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)

  5. A Fuzzy Control System for Inductive Video Games

    OpenAIRE

    Lara-Alvarez, Carlos; Mitre-Hernandez, Hugo; Flores, Juan; Fuentes, Maria

    2017-01-01

    It has been shown that the emotional state of students has an important relationship with learning; for instance, engaged concentration is positively correlated with learning. This paper proposes the Inductive Control (IC) for educational games. Unlike conventional approaches that only modify the game level, the proposed technique also induces emotions in the player for supporting the learning process. This paper explores a fuzzy system that analyzes the players' performance and their emotion...

  6. SPEED CONTROL OF DC MOTOR ON LOAD USING FUZZY LOGIC ...

    African Journals Online (AJOL)

    This paper presents the development of a fuzzy logic controller for the driver DC motor in the lube oil system of the H25 Hitachi gas turbine generator. The turbine generator is required to run at an operating pressure of 1.5bar with the low and the high pressure trip points being 0.78 bar and 1.9 bar respectively. However, the ...

  7. 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.

  8. Performance analysis of PM synchronous motor using fuzzy logic and self tuning fuzzy PI speed controls

    International Nuclear Information System (INIS)

    Karakaya, A.; Karakas, E.

    2008-01-01

    Permanent Magnet Synchronous Motors have nonlinear characteristics whose dynamics changes with time. In spite of this structure the permanent magnet synchronous motor has answered engineering problems in industry such as motion control which need high torque values. This paper obtains a nonlinear mathematical model for Permanent Magnet Synchronous Motor and realizes stimulation of the obtained model in the Matlab/Simulink program. Motor parameters are determined by an experimental set-up and they are used in the motor model. Speed control of motor model is made with Fuzzy Logic and Self Tuning logic PI controllers. Using the speed graphs obtained, rise time, overshoot, steady-state error and settling time are analyzed and controller performances are compared. (author)

  9. Stability Analysis of Positive Polynomial Fuzzy-Model-Based Control Systems with Time Delay under Imperfect Premise Matching

    OpenAIRE

    Li, Xiaomiao; Lam, Hak Keung; Song, Ge; Liu, Fucai

    2017-01-01

    This paper deals with the stability and positivity analysis of polynomial-fuzzy-model-based ({PFMB}) control systems with time delay, which is formed by a polynomial fuzzy model and a polynomial fuzzy controller connected in a closed loop, under imperfect premise matching. To improve the design and realization flexibility, the polynomial fuzzy model and the polynomial fuzzy controller are allowed to have their own set of premise membership functions. A sum-of-squares (SOS)-based stability ana...

  10. 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.

  11. Horizontal and Vertical Rule Bases Method in Fuzzy Controllers

    Directory of Open Access Journals (Sweden)

    Sadegh Aminifar

    2013-01-01

    Full Text Available Concept of horizontal and vertical rule bases is introduced. Using this method enables the designers to look for main behaviors of system and describes them with greater approximations. The rules which describe the system in first stage are called horizontal rule base. In the second stage, the designer modulates the obtained surface by describing needed changes on first surface for handling real behaviors of system. The rules used in the second stage are called vertical rule base. Horizontal and vertical rule bases method has a great roll in easing of extracting the optimum control surface by using too lesser rules than traditional fuzzy systems. This research involves with control of a system with high nonlinearity and in difficulty to model it with classical methods. As a case study for testing proposed method in real condition, the designed controller is applied to steaming room with uncertain data and variable parameters. A comparison between PID and traditional fuzzy counterpart and our proposed system shows that our proposed system outperforms PID and traditional fuzzy systems in point of view of number of valve switching and better surface following. The evaluations have done both with model simulation and DSP implementation.

  12. Car-Like Mobile Robot Oriented Positioning by Fuzzy Controllers

    Directory of Open Access Journals (Sweden)

    Noureddine Ouadah

    2008-11-01

    Full Text Available In this paper, fuzzy logic controllers (FLC are used to implement an efficient and accurate positioning of an autonomous car-like mobile robot, respecting final orientation. To accomplish this task, called "Oriented Positioning", two FLC have been developed: robot positioning controller (RPC and robot following controller (RFC. Computer simulation results illustrate the effectiveness of the proposed technique. Finally, real-time experiments have been made on an autonomous car-like mobile robot called "Robucar", developed to perform people transportation. Obtained results from experiments demonstrate the effectiveness of the proposed control strategy.

  13. Car-Like Mobile Robot Oriented Positioning by Fuzzy Controllers

    Directory of Open Access Journals (Sweden)

    Noureddine Ouadah

    2008-09-01

    Full Text Available In this paper, fuzzy logic controllers (FLC are used to implement an efficient and accurate positioning of an autonomous car-like mobile robot, respecting final orientation. To accomplish this task, called “Oriented Positioning”, two FLC have been developed: robot positioning controller (RPC and robot following controller (RFC. Computer simulation results illustrate the effectiveness of the proposed technique. Finally, real-time experiments have been made on an autonomous car-like mobile robot called “Robucar”, developed to perform people transportation. Obtained results from experiments demonstrate the effectiveness of the proposed control strategy.

  14. Buck supplies output voltage ripple reduction using fuzzy control

    Directory of Open Access Journals (Sweden)

    Nicu BIZON

    2007-12-01

    Full Text Available Using the PWM control for switching power supplies the peaks EMI noise appear at the switching frequency and its harmonics. Using randomize or chaotic PWM control techniques in these systems the power spectrum is spread out in all frequencies band spectral emissions, but with a bigger ripple in the output voltage. The proposed nonlinear feedback control method, which induces chaos, is based by fuzzy rules that minimize the output voltage ripple. The feasibility and effectiveness of this relative simple method is shown by simulation. A comparison with the previous control method is included, too.

  15. CLOSED LOOP CONTROL OF EMBEDDED Z-SOURCE INVERTER WITH FUZZY CONTROLLER FOR SOLAR PV APPLICATIONS

    OpenAIRE

    Midde Mahesh*, K. Leleedhar Rao

    2017-01-01

    This paper proposes the use of Embedded Z –source inverter system with fuzzy controller for Solar Photo Voltaic (PV) applications with adjustable speed drives. Closed loop operation FUZZY control strategies of EZSI system are proposed. EZSI produces the same voltage gain as Z-source inverter (ZSI) but due to the DC sources embedded within the X- shaped impedance network, it has the added advantage of inherent source filtering capability and also reduced capacitor sizing. This can be achiev...

  16. 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.

  17. 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.

  18. State-feedback control of fuzzy discrete-event systems.

    Science.gov (United States)

    Lin, Feng; Ying, Hao

    2010-06-01

    In a 2002 paper, we combined fuzzy logic with discrete-event systems (DESs) and established an automaton model of fuzzy DESs (FDESs). The model can effectively represent deterministic uncertainties and vagueness, as well as human subjective observation and judgment inherent to many real-world problems, particularly those in biomedicine. We also investigated optimal control of FDESs and applied the results to optimize HIV/AIDS treatments for individual patients. Since then, other researchers have investigated supervisory control problems in FDESs, and several results have been obtained. These results are mostly derived by extending the traditional supervisory control of (crisp) DESs, which are string based. In this paper, we develop state-feedback control of FDESs that is different from the supervisory control extensions. We use state space to describe the system behaviors and use state feedback in control. Both disablement and enforcement are allowed. Furthermore, we study controllability based on the state space and prove that a controller exists if and only if the controlled system behavior is (state-based) controllable. We discuss various properties of the state-based controllability. Aside from novelty, the proposed new framework has the advantages of being able to address a wide range of practical problems that cannot be effectively dealt with by existing approaches. We use the diabetes treatment as an example to illustrate some key aspects of our theoretical results.

  19. 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)

  20. 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.)

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. Review of Recent Type-2 Fuzzy Controller Applications

    Directory of Open Access Journals (Sweden)

    Kevin Tai

    2016-06-01

    Full Text Available Type-2 fuzzy logic controllers (T2 FLC can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

  6. 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.

  7. Fuzzy Approximate Model for Distributed Thermal Solar Collectors Control

    KAUST Repository

    Elmetennani, Shahrazed

    2014-07-01

    This paper deals with the problem of controlling concentrated solar collectors where the objective consists of making the outlet temperature of the collector tracking a desired reference. The performance of the novel approximate model based on fuzzy theory, which has been introduced by the authors in [1], is evaluated comparing to other methods in the literature. The proposed approximation is a low order state representation derived from the physical distributed model. It reproduces the temperature transfer dynamics through the collectors accurately and allows the simplification of the control design. Simulation results show interesting performance of the proposed controller.

  8. 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.

  9. Fuzzy Sarsa with Focussed Replacing Eligibility Traces for Robust and Accurate Control

    Science.gov (United States)

    Kamdem, Sylvain; Ohki, Hidehiro; Sueda, Naomichi

    Several methods of reinforcement learning in continuous state and action spaces that utilize fuzzy logic have been proposed in recent years. This paper introduces Fuzzy Sarsa(λ), an on-policy algorithm for fuzzy learning that relies on a novel way of computing replacing eligibility traces to accelerate the policy evaluation. It is tested against several temporal difference learning algorithms: Sarsa(λ), Fuzzy Q(λ), an earlier fuzzy version of Sarsa and an actor-critic algorithm. We perform detailed evaluations on two benchmark problems : a maze domain and the cart pole. Results of various tests highlight the strengths and weaknesses of these algorithms and show that Fuzzy Sarsa(λ) outperforms all other algorithms tested for a larger granularity of design and under noisy conditions. It is a highly competitive method of learning in realistic noisy domains where a denser fuzzy design over the state space is needed for a more precise control.

  10. 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.

  11. 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.

  12. 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 ...

  13. 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

  14. 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

  15. Fuzzy control applied to nuclear power plant pressurizer system

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Mauro V.; Almeida, Jose C.S., E-mail: mvitor@ien.gov.b, E-mail: jcsa@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

    2011-07-01

    In a pressurized water reactor (PWR) nuclear power plants (NPPs) the pressure control in the primary loop is very important for keeping the reactor in a safety condition and improve the generation process efficiency. The main component responsible for this task is the pressurizer. The pressurizer pressure control system (PPCS) utilizes heaters and spray valves to maintain the pressure within an operating band during steady state conditions, and limits the pressure changes, during transient conditions. Relief and safety valves provide overpressure protection for the reactor coolant system (RCS) to ensure system integrity. Various protective reactor trips are generated if the system parameters exceed safe bounds. Historically, a proportional-integral derivative (PID) controller is used in PWRs to keep the pressure in the set point, during those operation conditions. The purpose of this study has two main goals: first is to develop a pressurizer model based on artificial neural networks (ANNs); second is to develop a fuzzy controller for the PWR pressurizer pressure, and compare its performance with the P controller. Data from a simulator PWR plant was used to test the ANN and the controllers as well. The reference simulator is a Westinghouse 3-loop PWR plant with a total thermal output of 2785 MWth. The simulation results show that the pressurizer ANN model response are in reasonable agreement with the simulated power plant, and the fuzzy controller built in this study has better performance compared to the P controller. (author)

  16. Fuzzy control applied to nuclear power plant pressurizer system

    International Nuclear Information System (INIS)

    Oliveira, Mauro V.; Almeida, Jose C.S.

    2011-01-01

    In a pressurized water reactor (PWR) nuclear power plants (NPPs) the pressure control in the primary loop is very important for keeping the reactor in a safety condition and improve the generation process efficiency. The main component responsible for this task is the pressurizer. The pressurizer pressure control system (PPCS) utilizes heaters and spray valves to maintain the pressure within an operating band during steady state conditions, and limits the pressure changes, during transient conditions. Relief and safety valves provide overpressure protection for the reactor coolant system (RCS) to ensure system integrity. Various protective reactor trips are generated if the system parameters exceed safe bounds. Historically, a proportional-integral derivative (PID) controller is used in PWRs to keep the pressure in the set point, during those operation conditions. The purpose of this study has two main goals: first is to develop a pressurizer model based on artificial neural networks (ANNs); second is to develop a fuzzy controller for the PWR pressurizer pressure, and compare its performance with the P controller. Data from a simulator PWR plant was used to test the ANN and the controllers as well. The reference simulator is a Westinghouse 3-loop PWR plant with a total thermal output of 2785 MWth. The simulation results show that the pressurizer ANN model response are in reasonable agreement with the simulated power plant, and the fuzzy controller built in this study has better performance compared to the P controller. (author)

  17. 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

  18. 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.

  19. 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)

  20. Active control of flexible structures using a fuzzy logic algorithm

    Science.gov (United States)

    Cohen, Kelly; Weller, Tanchum; Ben-Asher, Joseph Z.

    2002-08-01

    This study deals with the development and application of an active control law for the vibration suppression of beam-like flexible structures experiencing transient disturbances. Collocated pairs of sensors/actuators provide active control of the structure. A design methodology for the closed-loop control algorithm based on fuzzy logic is proposed. First, the behavior of the open-loop system is observed. Then, the number and locations of collocated actuator/sensor pairs are selected. The proposed control law, which is based on the principles of passivity, commands the actuator to emulate the behavior of a dynamic vibration absorber. The absorber is tuned to a targeted frequency, whereas the damping coefficient of the dashpot is varied in a closed loop using a fuzzy logic based algorithm. This approach not only ensures inherent stability associated with passive absorbers, but also circumvents the phenomenon of modal spillover. The developed controller is applied to the AFWAL/FIB 10 bar truss. Simulated results using MATLAB© show that the closed-loop system exhibits fairly quick settling times and desirable performance, as well as robustness characteristics. To demonstrate the robustness of the control system to changes in the temporal dynamics of the flexible structure, the transient response to a considerably perturbed plant is simulated. The modal frequencies of the 10 bar truss were raised as well as lowered substantially, thereby significantly perturbing the natural frequencies of vibration. For these cases, too, the developed control law provides adequate settling times and rates of vibrational energy dissipation.

  1. Improved Rotor Speed Brushless DC Motor Using Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Jafar Mostafapour

    2015-04-01

    Full Text Available A brushless DC (BLDC Motors have advantages over brushed, Direct current (DC Motors and , Induction motor (IM. They have better speed verses torque characteristics, high dynamic response, high efficiency, long operating life, noiseless operation, higher speed ranges, and rugged construction. Also, torque delivered to motor size is higher, making it useful in application where space and weight are critical factors. With these advantages BLDC motors find wide spread application in automotive appliance, aerospace medical, and instrumentation and automation industries This paper can be seen as fuzzy controllers compared to PI control BLDC motor rotor speed has improved significantly and beter result can be achieve.

  2. 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.

  3. Nonlinear Robust Control of a Hypersonic Flight Vehicle Using Fuzzy Disturbance Observer

    Directory of Open Access Journals (Sweden)

    Lei Zhengdong

    2013-01-01

    Full Text Available This paper is concerned with a novel tracking controller design for a hypersonic flight vehicle in complex and volatile environment. The attitude control model is challengingly constructed with multivariate uncertainties and external disturbances, such as structure dynamic and stochastic wind disturbance. In order to resist the influence of uncertainties and disturbances on the flight control system, nonlinear disturbance observer is introduced to estimate them. Moreover, for the sake of high accuracy and sensitivity, fuzzy theory is adopted to improve the performance of the nonlinear disturbance observer. After the total disturbance is eliminated by dynamic inversion method, a cascade system is obtained and then stabilized by a sliding-mode controller. Finally, simulation results show that the strong robust controller achieves excellent performance when the closed-loop control system is influenced by mass uncertainties and external disturbances.

  4. Control of Rotary Cranes Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Amjed A. Al-mousa

    2003-01-01

    Full Text Available Rotary cranes (tower cranes are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them has become difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control attractive.

  5. 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.

  6. 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.

  7. Energy Management System Based on Fuzzy fractional order PID Controller for Transient Stability Improvement in Microgrids with Energy Storage

    DEFF Research Database (Denmark)

    Moafi, Milad; Marzband, Mousa; Savaghebi, Mehdi

    2016-01-01

    in the islanded Microgrid (MG). To increase performance for a wide range of power system operating conditions, an energy management systems (EMS) is proposed based on a fuzzy fractional order PID (FFOPID) controller. It is able to analyze and simulate the dynamic behavior in grid connected MGs. This controller...... is proposed in the MG encompassing distributed generation resources with “plug and play” ability. The performance of FFOPID controller is verified for frequency control purposes and to support internal bus voltage in both islanded and grid connected operating modes in accordance with the failure time. Energy...... combined with a PID-controller (termed as FLPID) and Fuzzy fractional order PID (termed as FFOPID) are implemented according to the characteristics and limitations of overloading and state of charge (SOC). The obtained results show good performance of FFOPID controllers by improving the transient stability...

  8. To the Problem of Electromechanical Interaction in Elevators with Controlled Electric Drive and Fuzzy Speed Controller

    Directory of Open Access Journals (Sweden)

    A. S. Koval

    2010-01-01

    Full Text Available The paper considers problems concerning electromechanical interaction in elevators with an adjustable asynchronous electric drive equipped with the vector control systems under direct torque control and direct torque control with pulse-width modulator. A mathematical description of electromechanical elevator system with due account of nonlinearity of the worm gear is given in the paper. The paper presents a simplified circuit design of a control system with a fuzzy speed controller. It has been established that the factor of electromechanical interaction in electromechanical system with the adjustable asynchronous electric drive and an fuzzy speed controller is within the range which corresponds to existence of the essential electromechanical interaction.

  9. 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...

  10. 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...

  11. Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

    Science.gov (United States)

    Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

    2016-01-01

    In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

  12. 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 ...

  13. 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 ...

  14. 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

  15. 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

  16. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

    Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.

  17. Four Degree Freedom Robot Arm with Fuzzy Neural Network Control

    Directory of Open Access Journals (Sweden)

    Şinasi Arslan

    2013-01-01

    Full Text Available In this study, the control of four degree freedom robot arm has been realized with the computed torque control method.. It is usually required that the four jointed robot arm has high precision capability and good maneuverability for using in industrial applications. Besides, high speed working and external applied loads have been acting as important roles. For those purposes, the computed torque control method has been developed in a good manner that the robot arm can track the given trajectory, which has been able to enhance the feedback control together with fuzzy neural network control. The simulation results have proved that the computed torque control with the neural network has been so successful in robot control.

  18. 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)

  19. 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.

  20. Feasibility analysis of fuzzy logic control for ITER Poloidal field (PF) AC/DC converter system

    Energy Technology Data Exchange (ETDEWEB)

    Hassan, Mahmood Ul; Fu, Peng [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Song, Zhiquan, E-mail: zhquansong@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Chen, Xiaojiao [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Zhang, Xiuqing [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Humayun, Muhammad [Shanghai Jiaotong University (China)

    2017-05-15

    Highlights: • The implementation of the Fuzzy controller for the ITER PF converter system is presented. • The comparison of the FLC and PI simulation are investigated. • The FLC single and parallel bridge operation are presented. • Fuzzification and Defuzzification algorithms are presented using FLC controller. - Abstract: This paper describes the feasibility analysis of the fuzzy logic control to increase the performance of the ITER poloidal field (PF) converter systems. A fuzzy-logic-based controller is designed for ITER PF converter system, using the traditional PI controller and Fuzzy controller (FC), the dynamic behavior and transient response of the PF converter system are compared under normal operation by analysis and simulation. The analysis results show that the fuzzy logic control can achieve better operation performance than PI control.

  1. Performance of Globally Linearized Controller and Two Region Fuzzy Logic Controller on a Nonlinear Process

    Directory of Open Access Journals (Sweden)

    N. Jaya

    2008-10-01

    Full Text Available In this work, a design and implementation of a Conventional PI controller, single region fuzzy logic controller, two region fuzzy logic controller and Globally Linearized Controller (GLC for a two capacity interacting nonlinear process is carried out. The performance of this process using single region FLC, two region FLC and GLC are compared with the performance of conventional PI controller about an operating point of 50 %. It has been observed that GLC and two region FLC provides better performance. Further, this procedure is also validated by real time experimentation using dSPACE.

  2. Neural-fuzzy control of adept one SCARA

    International Nuclear Information System (INIS)

    Er, M.J.; Toh, B.H.; Toh, B.Y.

    1998-01-01

    This paper presents an Intelligent Control Strategy for the Adept One SCARA (Selective Compliance Assembly Robot Arm). It covers the design and simulation study of a Neural-Fuzzy Controller (NFC) for the SCARA with a view of tracking a predetermined trajectory of motion in the joint space. The SCARA was simulated as a three-axis manipulator with the dynamics of the tool (fourth link) neglected and the mass of the load incorporated into the mass of the third link. The overall performance of the control system under different conditions, namely variation in playload, variations in coefficients of static, dynamic and viscous friction and different trajectories were studied and comparison made with an existing Neural Network Controller and two Computed Torque Controllers. The NFC was shown to be robust and is able to overcome the drawback of the existing Neural Network Controller

  3. Energy Management of Dual-Source Propelled Electric Vehicle using Fuzzy Controller Optimized via Genetic Algorithm

    DEFF Research Database (Denmark)

    Khoobi, Saeed; Halvaei, Abolfazl; Hajizadeh, Amin

    2016-01-01

    Energy and power distribution between multiple energy sources of electric vehicles (EVs) is the main challenge to achieve optimum performance from EV. Fuzzy inference systems are powerful tools due to nonlinearity and uncertainties of EV system. Design of fuzzy controllers for energy management...... of EV relies too much on the expert experience and it may lead to sub-optimal performance. This paper develops an optimized fuzzy controller using genetic algorithm (GA) for an electric vehicle equipped with two power bank including battery and super-capacitor. The model of EV and optimized fuzzy...

  4. 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)

  5. Local Model Predictive Control for T-S Fuzzy Systems.

    Science.gov (United States)

    Lee, Donghwan; Hu, Jianghai

    2017-09-01

    In this paper, a new linear matrix inequality-based model predictive control (MPC) problem is studied for discrete-time nonlinear systems described as Takagi-Sugeno fuzzy systems. A recent local stability approach is applied to improve the performance of the proposed MPC scheme. At each time k , an optimal state-feedback gain that minimizes an objective function is obtained by solving a semidefinite programming problem. The local stability analysis, the estimation of the domain of attraction, and feasibility of the proposed MPC are proved. Examples are given to demonstrate the advantages of the suggested MPC over existing approaches.

  6. Fuzzy Networked Control Systems Design Considering Scheduling Restrictions

    Directory of Open Access Journals (Sweden)

    H. Benítez-Pérez

    2012-01-01

    known a priory but from a dynamic real-time behavior. To do so, the use of priority dynamic Priority exchange scheduling is performed. The objective of this paper is to show a way to tackle multiple time delays that are bounded and the dynamic response from real-time scheduling approximation. The related control law is designed considering fuzzy logic approximation for nonlinear time delays coupling, where the main advantage is the integration of this behavior through extended state space representation keeping certain linear and bounded behavior and leading to a stable situation during events presentation by guaranteeing stability through Lyapunov.

  7. Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

    OpenAIRE

    Hamed Kharrati; Sohrab Khanmohammadi; Witold Pedrycz; Ghasem Alizadeh

    2012-01-01

    This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB) systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA) is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy m...

  8. 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.

  9. Applications of sliding mode control

    CERN Document Server

    Ghommam, Jawhar; Zhu, Quanmin

    2017-01-01

    This book presents essential studies and applications in the context of sliding mode control, highlighting the latest findings from interdisciplinary theoretical studies, ranging from computational algorithm development to representative applications. Readers will learn how to easily tailor the techniques to accommodate their ad hoc applications. To make the content as accessible as possible, the book employs a clear route in each paper, moving from background to motivation, to quantitative development (equations), and lastly to case studies/illustrations/tutorials (simulations, experiences, curves, tables, etc.). Though primarily intended for graduate students, professors and researchers from related fields, the book will also benefit engineers and scientists from industry. .

  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. 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.

  12. 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...

  13. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...

  14. 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-...

  15. 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.

  16. Less Conservative ℋ∞ Fuzzy Control for Discrete-Time Takagi-Sugeno Systems

    Directory of Open Access Journals (Sweden)

    Leonardo Amaral Mozelli

    2011-01-01

    Full Text Available New analysis and control design conditions of discrete-time fuzzy systems are proposed. Using fuzzy Lyapunov's functions and introducing slack variables, less conservative conditions are obtained. The controller guarantees system stabilization and ℋ∞ performance. Numerical tests and a practical experiment in Chua's circuit are presented to show the effectiveness.

  17. A fuzzy controlled three-phase centrifuge for waste separation

    International Nuclear Information System (INIS)

    Parkinson, W.J.; Smith, R.E.; Miller, N.

    1998-02-01

    The three-phase centrifuge technology discussed in this paper was developed by Neal Miller, president of Centech, Inc. The three-phase centrifuge is an excellent device for cleaning up oil field and refinery wastes which are typically composed of hydrocarbons, water, and solids. The technology is unique. It turns the waste into salable oil, reusable water, and landfill-able solids. No secondary waste is produced. The problem is that only the inventor can set up and run the equipment well enough to provide an optimal cleanup. Demand for this device has far exceeded a one man operation. There is now a need for several centrifuges to be operated at different locations at the same time. This has produced a demand for an intelligent control system, one that could replace a highly skilled operator, or at least supplement the skills of a less experienced operator. The control problem is ideally suited to fuzzy logic, since the centrifuge is a highly complicated machine operated entirely by the skill and experience of the operator. A fuzzy control system was designed for and used with the centrifuge

  18. 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.

  19. 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)

  20. 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

  1. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    Science.gov (United States)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

  2. Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications

    Directory of Open Access Journals (Sweden)

    A.A. Fahmy

    2013-12-01

    Full Text Available This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.

  3. 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.

  4. 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.).

  5. 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)

  6. 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.

  7. Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation

    Directory of Open Access Journals (Sweden)

    Dongjie Li

    2012-01-01

    Full Text Available The interactive nanomanipulation platform is established based on fuzzy control and connected region marking (CRM algorithm in SEM. The 3D virtual nanomanipulation model is developed to make up the insufficiency of the 2D SEM image information, which provides the operator with depth and real-time visual feedback information to guide the manipulation. The haptic device Omega3 is used as the master to control the 3D motion of the nanopositioner in master-slave mode and offer the force sensing to the operator controlled with fuzzy control algorithm. Aiming at sensing of force feedback during the nanomanipulation, the collision detection method of the virtual nanomanipulation model and the force rending model are studied to realize the force feedback of nanomanipulation. The CRM algorithm is introduced to process the SEM image which provides effective position data of the objects for updating the virtual environment (VE, and relevant issues such as calibration and update rate of VE are also discussed. Finally, the performance of the platform is validated by the ZnO nanowire manipulation experiments.

  8. Silicon microgyroscope temperature prediction and control system based on BP neural network and Fuzzy-PID control method

    International Nuclear Information System (INIS)

    Xia, Dunzhu; Kong, Lun; Hu, Yiwei; Ni, Peizhen

    2015-01-01

    We present a novel silicon microgyroscope (SMG) temperature prediction and control system in a narrow space. As the temperature of SMG is closely related to its drive mode frequency and driving voltage, a temperature prediction model can be established based on the BP neural network. The simulation results demonstrate that the established temperature prediction model can estimate the temperature in the range of −40 to 60 °C with an error of less than ±0.05 °C. Then, a temperature control system based on the combination of fuzzy logic controller and the increment PID control method is proposed. The simulation results prove that the Fuzzy-PID controller has a smaller steady state error, less rise time and better robustness than the PID controller. This is validated by experimental results that show the Fuzzy-PID control method can achieve high precision in keeping the SMG temperature stable at 55 °C with an error of less than 0.2 °C. The scale factor can be stabilized at 8.7 mV/°/s with a temperature coefficient of 33 ppm °C −1 . ZRO (zero rate output) instability is decreased from 1.10°/s (9.5 mV) to 0.08°/s (0.7 mV) when the temperature control system is implemented over an ambient temperature range of −40 to 60 °C. (paper)

  9. Controlling Smart Green House Using Fuzzy Logic Method

    Directory of Open Access Journals (Sweden)

    Rafiuddin Syam

    2017-03-01

    Full Text Available To increase agricultural output it is needed a system that can help the environmental conditions for optimum plant growth. Smart greenhouse allows for plants to grow optimally, because the temperature and humidity can be controlled so that no drastic changes. It is necessary for optimal smart greenhouse needed a system to manipulate the environment in accordance with the needs of the plant. In this case the setting temperature and humidity in the greenhouse according to the needs of the plant. So using an automated system for keeping such environmental condition is important. In this study, the authors use fuzzy logic to make the duration of watering the plants more dynamic in accordance with the input temperature and humidity so that the temperature and humidity in the green house plants maintained in accordance to the reference condition. Based on the experimental results using fuzzy logic method is effective to control the duration of watering and to maintain the optimum temperature and humidity inside the greenhouse

  10. Controlling Smart Green House Using Fuzzy Logic Method

    Directory of Open Access Journals (Sweden)

    Rafiuddin Syam

    2015-10-01

    Full Text Available To increase agricultural output it is needed a system that can help the environmental conditions for optimum plant growth. Smart greenhouse allows for plants to grow optimally, because the temperature and humidity can be controlled so that no drastic changes. It is necessary for optimal smart greenhouse needed a system to manipulate the environment in accordance with the needs of the plant. In this case the setting temperature and humidity in the greenhouse according to the needs of the plant. So using an automated system for keeping such environmental condition is important. In this study, the authors use fuzzy logic to make the duration of watering the plants more dynamic in accordance with the input temperature and humidity so that the temperature and humidity in the green house plants maintained in accordance to the reference condition. Based on the experimental results using fuzzy logic method is effective to control the duration of watering and to maintain the optimum temperature and humidity inside the greenhouse

  11. On the application of bezier surfaces for GA-Fuzzy controller design for use in automatic generation control

    CSIR Research Space (South Africa)

    Boesack, CD

    2012-03-01

    Full Text Available Automatic Generation Control (AGC) of large interconnected power systems are typically controlled by a PI or PID type control law. Recently intelligent control techniques such as GA-Fuzzy controllers have been widely applied within the power...

  12. 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)

  13. 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.

  14. 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:.

  15. 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...

  16. 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.

  17. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  19. Fuzzy Impulsive Control of Permanent Magnet Synchronous Motors

    International Nuclear Information System (INIS)

    Dong, Li; Shi-Long, Wang; Xiao-Hong, Zhang; Dan, Yang; Hui, Wang

    2008-01-01

    The permanent magnet synchronous motors (PMSMs) may experience chaotic behaviours with systemic parameters falling into a certain area or under certain working conditions, which threaten the secure and stable operation of motor-driven. Hence, it is important to study the methods of controlling or suppressing chaos in PMSMs. In this work, the Takagi–Sugeno (T-S) fuzzy impulsive control model for PMSMs is established via the T-S modelling methodology and impulsive technology. Based on the new model, the control conditions of asymptotical stability and exponential stability for PMSMs have been derived by the Lyapunov method. Finally, an illustrated example is also given to show the effectiveness of the obtained results

  20. Gain Scheduling of PID Controller Based on Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Singh Sandeep

    2016-01-01

    Full Text Available This paper aims to utilize fuzzy rules and reasoning to determine the controller parameters and the PID controller generates the control signal. The objective of this study is to simulate the proposed scheme on various processes and arrive at results providing better response of the system when compared with best industrial auto-tuning technique: Ziegler-Nichols. The proposed scheme is based upon the Ultimate Gain (Ku and the Period (Tu of the system. The error and rate of change in error gains are tuned manually to get the desired response using LabVIEW. This can also be done with various optimization techniques. A thumb rule for choosing the ranges for Kc, Kd and Ki has been obtained experimentally.

  1. 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

  2. ANALISIS TOTAL PRODUCTIVE MAINTENANCE DENGAN METODE OVERALL EQUIPMENT EFFECTIVENESS DAN FUZZY FAILURE MODE AND EFFECTS ANALYSIS

    Directory of Open Access Journals (Sweden)

    Supriyadi Supriyadi

    2017-11-01

    Full Text Available Ash Handling System merupakan suatu bagian dari pembangkit listrik tenaga uap dengan bahan bakar batu bara yang berfungsi untuk menyalurkan limbah pembuangan sisa hasil proses pembakaran batu bara pada boiler. Sisa pembakaran terbagi menjadi fly ash dan bottom ash. Untuk sisa pembakaran fly ash akan disalurkan menuju ke Electrostatic Precipitator untuk ditangkap  dengan metode corona dan ditransfer menuju penampungan fly ash dengan cara dimampatkan bersama udara dari kompresor yang melalui pipa-pipa dan tabung transporter. Sedangkan untuk sisa pembakaran bottom ash akan disalurkan dengan alat yang disebut SSC (Submerged Scraper Conveyor. Gangguan pada SSC dapat terjadi mulai dari belt putus, masalah pada penggerak, hingga masalah pada kelistrikan dan instrumennya. Penelitian ini bertujuan untuk mengetahui nilai OEE, mengetahui dampak gangguan belt sobek, mengetahui penyebab terjadinya belt conveyor sobek dan melakukan estimasi hasil perbaikan dari sisi biaya. Penelitian ini menggunakan metode Overall Equipment Effectiveness (OEE dan Fuzzy Failure Mode and Effects Analysis. Hasil penelitian menunjukkan bahwa rata-rata nilai OEE pada tahun 2015 sekitar 52,05%, masih di bawah standar nilai OEE sebesar 85%. Penyebab utamanya adalah adanya gangguan belt sobek karena gesekan belt dengan support return ketika belt conveyor mengalami jogging yang berdampak pada terganggunya penyaluran abu. Modifikasi dapat menghindari kerugian perusahaan sebesar Rp. 582.548.800,00.

  3. Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

    Science.gov (United States)

    Borni, A.; Abdelkrim, T.; Zaghba, L.; Bouchakour, A.; Lakhdari, A.; Zarour, L.

    2017-02-01

    In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved.

  4. 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.

  5. Chattering-free sliding mode observer for speed sensorless control of PMSM

    Directory of Open Access Journals (Sweden)

    Mahmoud M. Gaballah

    2017-07-01

    Full Text Available This article presents a new speed observer based on fuzzy logic for speed sensorless control applications of permanent magnet synchronous motor “PMSM”. The switch function in traditional Sliding Mode Observer “SMO” is replaced by a rule based fuzzy logic system. The proposed observer not only improves the system dynamic performance during disturbances or parameter variations, but also has a high accuracy tracking performance with sufficient chattering reduction. The validity of the new observer corroborated through experimental results using TMS320F28069M Digital Signal Processor “DSP”.

  6. 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.

  7. 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.

  8. Comparison of Energy Consumption in the Classical (PID and Fuzzy Control of Foundry Resistance Furnace

    Directory of Open Access Journals (Sweden)

    Ziółkowskia E.

    2012-09-01

    Full Text Available Foundry resistance furnaces are thermal devices with a relatively large time delay in their response to a change in power parameters. Commonly used in automation classical PID controllers do not meet the requirements of high-quality control. Developed in recent years, fuzzy control theory is increasingly being used in various branches of economy and industry. Fuzzy controllers allow to introduce new developments in control systems of foundry furnaces as well. Correctly selected fuzzy controller can significantly reduce energy consumption in a controlled thermal process of heating equipment. The article presents a comparison of energy consumption by control system of foundry resistance furnace, equipped with either a PID controller or fuzzy controller optimally chosen.

  9. New concept of direct torque neuro-fuzzy control for induction motor drives. Simulation study

    Energy Technology Data Exchange (ETDEWEB)

    Grabowski, P.Z. [Institute of Control and Industrial Electronics, Warsaw University of Technology, Warsaw (Poland)

    1997-12-31

    This paper presents a new control strategy in the discrete Direct Torque Control (DTC) based on neuro-fuzzy structure. Two schemes are proposed: neuro-fuzzy switching times calculator and neuro-fuzzy incremental controller with space vector modulator. These control strategies guarantee very good dynamic and steady-states characteristics, with very low sampling time and constant switching frequency. The proposed techniques are verified by simulation study of the whole drive system and results are compared with conventional discrete Direct Torque Control method. (orig.) 18 refs.

  10. Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

    Directory of Open Access Journals (Sweden)

    Hamed Kharrati

    2012-01-01

    Full Text Available This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy modeling. To validate the model, a controller based on proposed polynomial fuzzy systems is designed and then applied to both original nonlinear plant and fuzzy model for comparison. Additionally, stability analysis for the proposed polynomial FMB control system is investigated employing Lyapunov theory and a sum of squares (SOS approach. Moreover, the form of the membership functions is considered in stability analysis. The SOS-based stability conditions are attained using SOSTOOLS. Simulation results are also given to demonstrate the effectiveness of the proposed method.

  11. 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

  12. Fuzzy logic

    CERN Document Server

    Smets, P

    1995-01-01

    We start by describing the nature of imperfect data, and giving an overview of the various models that have been proposed. Fuzzy sets theory is shown to be an extension of classical set theory, and as such has a proeminent role or modelling imperfect data. The mathematic of fuzzy sets theory is detailled, in particular the role of the triangular norms. The use of fuzzy sets theory in fuzzy logic and possibility theory,the nature of the generalized modus ponens and of the implication operator for approximate reasoning are analysed. The use of fuzzy logic is detailled for application oriented towards process control and database problems.

  13. Fuzzy-based failure mode and effect analysis (FMEA) of a hybrid molten carbonate fuel cell (MCFC) and gas turbine system for marine propulsion

    Science.gov (United States)

    Ahn, Junkeon; Noh, Yeelyong; Park, Sung Ho; Choi, Byung Il; Chang, Daejun

    2017-10-01

    This study proposes a fuzzy-based FMEA (failure mode and effect analysis) for a hybrid molten carbonate fuel cell and gas turbine system for liquefied hydrogen tankers. An FMEA-based regulatory framework is adopted to analyze the non-conventional propulsion system and to understand the risk picture of the system. Since the participants of the FMEA rely on their subjective and qualitative experiences, the conventional FMEA used for identifying failures that affect system performance inevitably involves inherent uncertainties. A fuzzy-based FMEA is introduced to express such uncertainties appropriately and to provide flexible access to a risk picture for a new system using fuzzy modeling. The hybrid system has 35 components and has 70 potential failure modes, respectively. Significant failure modes occur in the fuel cell stack and rotary machine. The fuzzy risk priority number is used to validate the crisp risk priority number in the FMEA.

  14. 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 (

  15. Fuzzy Logic Based Control of Power of PEM Fuel Cell System for Residential Application

    Directory of Open Access Journals (Sweden)

    Khaled MAMMAR

    2009-07-01

    Full Text Available This paper presents a dynamic model of Fuel cell system for residential power generation. The models proposed include a fuel cell stack model, reformer model and DC/AC inverter model. Furthermore a fuzzy logic (FLC controller is used to control active power of PEM fuel cell system. The controller modifies the hydrogen flow feedback from the terminal load. Simulation results confirmed the high performance capability of the fuzzy logic controller to control power generation.

  16. Fuzzy Controller Design Using FPGA for Photovoltaic Maximum Power Point Tracking

    OpenAIRE

    Basil M Hamed; Mohammed S. El-Moghany

    2012-01-01

    The cell has optimum operating point to be able to get maximum power. To obtain Maximum Power from photovoltaic array, photovoltaic power system usually requires Maximum Power Point Tracking (MPPT) controller. This paper provides a small power photovoltaic control system based on fuzzy control with FPGA technology design and implementation for MPPT. The system composed of photovoltaic module, buck converter and the fuzzy logic controller implemented on FPGA for controlling on/off time of MOSF...

  17. 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

  18. FUZZY-GENETIC CONTROL OF QUADROTOR UNMANNED AERIAL VEHICLES

    Directory of Open Access Journals (Sweden)

    Attila Nemes

    2016-03-01

    Full Text Available This article presents a novel fuzzy identification method for dynamic modelling of quadrotor unmanned aerial vehicles. The method is based on a special parameterization of the antecedent part of fuzzy systems that results in fuzzy-partitions for antecedents. This antecedent parameter representation method of fuzzy rules ensures upholding of predefined linguistic value ordering and ensures that fuzzy-partitions remain intact throughout an unconstrained hybrid evolutionary and gradient descent based optimization process. In the equations of motion the first order derivative component is calculated based on Christoffel symbols, the derivatives of fuzzy systems are used for modelling the Coriolis effects, gyroscopic and centrifugal terms. The non-linear parameters are subjected to an initial global evolutionary optimization scheme and fine tuning with gradient descent based local search. Simulation results of the proposed new quadrotor dynamic model identification method are promising.

  19. Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control

    Directory of Open Access Journals (Sweden)

    Allaoua Boumediene

    2008-01-01

    Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.

  20. Fuzzy Control of Yaw and Roll Angles of a Simulated Helicopter Model Includes Articulated Manipulators

    Directory of Open Access Journals (Sweden)

    Hossein Sadegh Lafmejani

    2015-09-01

    Full Text Available Fuzzy logic controller (FLC is a heuristic method by If-Then Rules which resembles human intelligence and it is a good method for designing Non-linear control systems. In this paper, an arbitrary helicopter model includes articulated manipulators has been simulated with Matlab SimMechanics toolbox. Due to the difficulties of modeling this complex system, a fuzzy controller with simple fuzzy rules has been designed for its yaw and roll angles in order to stabilize the helicopter while it is in the presence of disturbances or its manipulators are moving for a task. Results reveal that a simple FLC can appropriately control this system.

  1. 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)

  2. 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.

  3. 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.

  4. 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.

  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. Single axis control of ball position in magnetic levitation system using fuzzy logic control

    Science.gov (United States)

    Sahoo, Narayan; Tripathy, Ashis; Sharma, Priyaranjan

    2018-03-01

    This paper presents the design and real time implementation of Fuzzy logic control(FLC) for the control of the position of a ferromagnetic ball by manipulating the current flowing in an electromagnet that changes the magnetic field acting on the ball. This system is highly nonlinear and open loop unstable. Many un-measurable disturbances are also acting on the system, making the control of it highly complex but interesting for any researcher in control system domain. First the system is modelled using the fundamental laws, which gives a nonlinear equation. The nonlinear model is then linearized at an operating point. Fuzzy logic controller is designed after studying the system in closed loop under PID control action. The controller is then implemented in real time using Simulink real time environment. The controller is tuned manually to get a stable and robust performance. The set point tracking performance of FLC and PID controllers were compared and analyzed.

  7. Inferring radar mode changes from elementary pulse features using Fuzzy ARTMAP classification

    CSIR Research Space (South Africa)

    Potgieter, PF

    2007-11-01

    Full Text Available ARTMAP The Fuzzy ARTMAP system is the interconnection between two fuzzy ART modules, ARTa and ARTb. The map field, Fab links these modules together to form predictive associations between categories and to perform match tracking [14...]. The interactions between the map field, Fab and other fuzzy ART modules are as follows. Inputs to the ARTa and ARTb modules are I = A = (a, ac) and I = B = (b, bc) respectively. Variables and vectors in ARTa and ARTb are denoted by superscript and subscript...

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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)

  14. 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.

  15. 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.

  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. 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)

  18. 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.

  19. 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.

  20. Fuzzy Sets-based Control Rules for Terminating Algorithms

    Directory of Open Access Journals (Sweden)

    Jose L. VERDEGAY

    2002-01-01

    Full Text Available In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the questions on the management of the fuzziness in some optimisation models, and then of using fuzzy rules for terminating conventional algorithms are presented, discussed and analyzed. Finally, for the concrete case of the Travelling Salesman Problem, and as an illustration of determination, management and using the fuzzy rules, a new algorithm easy to implement in the Model-Base Management System of any oriented Decision Support System is shown.

  1. CONTROL TEMPERATURE ON PLANT BABY INCUBATOR WITH FUZZY LOGIC

    Directory of Open Access Journals (Sweden)

    Noor Yulita Dwi Setyaningsih

    2016-04-01

    Full Text Available Inkubator bayi merupakan salah satu media medis yang digunakan untuk menjaga kondisi suhu dari bayi prematur atau bayi yang baru lahir. Suhu merupakan salah satu faktor yang sangat penting untuk dijaga bagi bayi baru lahir, karena kondisi bayi baru lahir yang tidak stabil dan belum bisa melakukan produksi panas sendiri untuk menghangatkan tubuhnya dan memproduksi panas untuk menjaga kestabilan tubuhnya. Kendali logika fuzzy digunakan untuk mengendalikan suhu pada penelitian ini, karena kebutuhan bayi yang berbeda-beda sehingga pemanfaatan sistem kendali fuzzy ini sangat mempermudah dalam melakukan pengendalian. Parameter yang digunakan dalam pengendalian ini adalah nilai Error, d-eror, dan sinyal kontrol. Hasil penggunaan sistem kendali logika fuzzy untuk pengendalian suhu pada plant inkubator bayi adalah kesalahan yang terjadi dapat dikurangi dan kestabilan dapat dipertahankan. Meskipun adanya gangguan yang diberikan pada sistem, dengan pemanfaatan sistem kendali fuzzy ini, dapat menjaga sistem pada keadaan yang stabil. Kata kunci: sistem kendali, temperature, inkubator bayi, plant, logika fuzzy, new born.

  2. A Two-Stage Fuzzy Logic Control Method of Traffic Signal Based on Traffic Urgency Degree

    OpenAIRE

    Yan Ge

    2014-01-01

    City intersection traffic signal control is an important method to improve the efficiency of road network and alleviate traffic congestion. This paper researches traffic signal fuzzy control method on a single intersection. A two-stage traffic signal control method based on traffic urgency degree is proposed according to two-stage fuzzy inference on single intersection. At the first stage, calculate traffic urgency degree for all red phases using traffic urgency evaluation module and select t...

  3. Operational Investigation of Overhead Crane with Fuzzy Logic Anti-Swing Controller Using 3-D Simulation

    Directory of Open Access Journals (Sweden)

    Y. N. Petrenko

    2011-01-01

    Full Text Available The purpose of a crane control system is to provide load transfer with minimum swinging. The paper presents a developed three-dimensional simulation model of a bridge crane with fuzzy logic controller designed with application of genetic algorithms. Comparative indices of oscillation while load transferring are given in the paper. The indices have been obtained at various parameters of the fuzzy logic controller

  4. 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

  5. Design and implementation of a new fuzzy PID controller for networked control systems.

    Science.gov (United States)

    Fadaei, A; Salahshoor, K

    2008-10-01

    This paper presents a practical network platform to design and implement a networked-based cascade control system linking a Smar Foundation Fieldbus (FF) controller (DFI-302) and a Siemens programmable logic controller (PLC-S7-315-2DP) through Industrial Ethernet to a laboratory pilot plant. In the presented network configuration, the Smar OPC tag browser and Siemens WinCC OPC Channel provide the communicating interface between the two controllers. The paper investigates the performance of a PID controller implemented in two different possible configurations of FF function block (FB) and networked control system (NCS) via a remote Siemens PLC. In the FB control system implementation, the desired set-point is provided by the Siemens Human-Machine Interface (HMI) software (i.e, WinCC) via an Ethernet Modbus link. While, in the NCS implementation, the cascade loop is realized in remote Siemens PLC station and the final element set-point is sent to the Smar FF station via Ethernet bus. A new fuzzy PID control strategy is then proposed to improve the control performances of the networked-based control systems due to an induced transmission delay degradation effect. The proposed strategy utilizes an innovative idea based on sectionalizing the error signal of the step response into three different functional zones. The supporting philosophy behind these three functional zones is to decompose the desired control objectives in terms of rising time, settling time and steady-state error measures maintained by an appropriate PID-type controller in each zone. Then, fuzzy membership factors are defined to configure the control signal on the basis of the fuzzy weighted PID outputs of all three zones. The obtained results illustrate the effectiveness of the proposed fuzzy PID control scheme in improving the performances of the implemented NCS for different transportation delays.

  6. Fuzzy colored petri nets and their application to efficient design and V and V of fuzzy logic controllers

    International Nuclear Information System (INIS)

    Son, Han Seong; Seong, Poong Hyun

    1998-01-01

    Generally, FLC design causes the designer to spend too much efforts and time. If a design support is provided to apply various membership functions to and simulate a FLC without coding at the early development stage, the cost problem may be solved to a remarkable degree. In order to offer the systematic approach to support FLC design, Fuzzy Colored Petri Nets (FCPN) is introduced as design support in this work. The feasibility of FCPN is demonstrated through a controller design example

  7. Speed Control Design of Permanent Magnet Synchronous Motor using TakagiSugeno Fuzzy Logic Control

    Directory of Open Access Journals (Sweden)

    Ahmad Asri Abd Samat

    2017-12-01

    Full Text Available This paper proposes a speed control design of Permanent Magnet Synchronous Motor (PMSM using Field Oriented Control (FOC. The focus is to design a speed control using Takagi — Sugeno Fuzzy Logic Control (T-S FLS. These systems will replace the conventional method which is proportional-integral (PI. The objective of this paper is to study the T—S Fuzzy Inference System (FIS speed regulator and acceleration observer for PMSM. The scope of study basically is to design and analyse the Takagi Sugeno FLC and the PMSM. This paper also will describe the methodology and process of modelling the PMSM including data analysis. The simulation work is implemented in Matlab-Simulink to verify the control method. The effectiveness of this proposed control method was confirmed through various range of speed and torque variation.

  8. Fuzzy variable impedance control based on stiffness identification for human-robot cooperation

    Science.gov (United States)

    Mao, Dachao; Yang, Wenlong; Du, Zhijiang

    2017-06-01

    This paper presents a dynamic fuzzy variable impedance control algorithm for human-robot cooperation. In order to estimate the intention of human for co-manipulation, a fuzzy inference system is set up to adjust the impedance parameter. Aiming at regulating the output fuzzy universe based on the human arm’s stiffness, an online stiffness identification method is developed. A drag interaction task is conducted on a 5-DOF robot with variable impedance control. Experimental results demonstrate that the proposed algorithm is superior.

  9. 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

  10. Modeling and fuzzy control of the engine coolant conditioning system in an IC engine test bed

    International Nuclear Information System (INIS)

    Mohtasebi, Seyed Saeid; Shirazi, Farzad A.; Javaheri, Ahmad; Nava, Ghodrat Hamze

    2010-01-01

    Mechanical and thermodynamical performance of internal combustion engines is significantly affected by the engine working temperature. In an engine test bed, the internal combustion engines are tested in different operating conditions using a dynamometer. It is required that the engine temperature be controlled precisely, particularly in transient states. This precise control can be achieved by an engine coolant conditioning system mainly consisting of a heat exchanger, a control valve, and a controller. In this study, constitutive equations of the system are derived first. These differential equations show the second- order nonlinear time-varying dynamics of the system. The model is validated with the experimental data providing satisfactory results. After presenting the dynamic equations of the system, a fuzzy controller is designed based on our prior knowledge of the system. The fuzzy rules and the membership functions are derived by a trial and error and heuristic method. Because of the nonlinear nature of the system the fuzzy rules are set to satisfy the requirements of the temperature control for different operating conditions of the engine. The performance of the fuzzy controller is compared with a PI one for different transient conditions. The results of the simulation show the better performance of the fuzzy controller. The main advantages of the fuzzy controller are the shorter settling time, smaller overshoot, and improved performance especially in the transient states of the system

  11. 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.

  12. 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.

  13. Development of Fuzzy Logic Control for Vehicle Air Conditioning System

    Directory of Open Access Journals (Sweden)

    Henry Nasution

    2008-08-01

    Full Text Available A vehicle air conditioning system is experimentally investigated. Measurements were taken during the experimental period at a time interval of one minute for a set point temperature of 22, 23 and 24oC with internal heat loads of 0, 1 and 2 kW. The cabin temperature and the speed of the compressor were varied and the performance of the system, energy consumption and energy saving ware analyzed. The main objective of the experimental work is to evaluate the energy saving obtained when the fuzzy logic control (FLC algorithm, through an inverter, continuously regulates the compressor speed. It demonstrates better control of the compressor operation in terms of energy consumption as compared to the control by using a thermostat imposing On/Off cycles on the compressor at the nominal frequency of 50 Hz. The experimental set-up consists of original components from the air conditioning system of a compact passenger vehicle. The experimental results indicate that the proposed technique can save energy and improve indoor comfort significantly for vehicle air conditioning systems compared to the conventional (On/Off control technique.

  14. Fuzzy Controlled Parallel AC-DC Converter for PFC

    Directory of Open Access Journals (Sweden)

    M Subba Rao

    2011-01-01

    Full Text Available Paralleling of converter modules is a well-known technique that is often used in medium-power applications to achieve the desired output power by using smaller size of high frequency transformers and inductors. In this paper, a parallel-connected single-phase PFC topology using flyback and forward converters is proposed to improve the output voltage regulation with simultaneous input power factor correction (PFC and control. The goal of the control is to stabilize the output voltage of the converter against the load variations. The paper presents the derivation of fuzzy control rules for the dc/dc converter circuit and control algorithm for regulating the dc/dc converter. This paper presents a design example and circuit analysis for 200 W power supply. The proposed approach offers cost effective, compact and efficient AC/DC converter by the use of parallel power processing. MATLAB/SIMULINK is used for implementation and simulation results show the performance improvement.

  15. Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Oussama Ait Sahed

    2015-01-01

    Full Text Available A fuzzy predictive controller using particle swarm optimization (PSO approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

  16. 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.

  17. evaluation of a multi-variable self-learning fuzzy logic controller

    African Journals Online (AJOL)

    Dr Obe

    2003-03-01

    Mar 1, 2003 ... The most challenging aspect of the design of a fuzzy logic controller is ... inaccuracy (or structured uncertainty) and unmodelled ... mathematical analysis on paper is impossible ... output (SISO) system that can self-construct ...

  18. Depth Control of Sevofluorane Anesthesia with Microcontroller Based Fuzzy Logic System

    National Research Council Canada - National Science Library

    Yardimci, A

    2001-01-01

    ... at the end of the anesthesia. In this study, sevofluorane depth of anesthesia was examined through a microcontroller-based fuzzy logic control system according to the blood pressure and heart rate taken from the patient...

  19. 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.

  20. Disturbance attenuation of nonlinear control systems using an observer-based fuzzy feedback linearization control

    International Nuclear Information System (INIS)

    Chen, C.-C.; Hsu, C.-H.; Chen, Y.-J.; Lin, Y.-F.

    2007-01-01

    The almost disturbance decoupling and trajectory tracking of nonlinear control systems using an observer-based fuzzy feedback linearization control (FLC) is developed. Because not all of the state variables of the nonlinear dynamic equations are available, a nonlinear state observer is employed to estimate the state variables. The feedback linearization control guarantees the almost disturbance decoupling performance and the uniform ultimate bounded stability of the tracking error system. Once the tracking errors are driven to touch the global final attractor with the desired radius, the fuzzy logic control is immediately applied via human expert's knowledge to improve the convergence rate. One example, which cannot be solved by the first paper on the almost disturbance decoupling problem, is proposed in this paper to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by our proposed approach. In order to demonstrate the practical applicability, the study has investigated a pendulum control system

  1. 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.

  2. MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique.

    Science.gov (United States)

    Kumarasabapathy, N; Manoharan, P S

    2015-01-01

    This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.

  3. 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.

  4. MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique

    Directory of Open Access Journals (Sweden)

    N. Kumarasabapathy

    2015-01-01

    Full Text Available This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs. The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.

  5. Robust Fuzzy Control for Fractional-Order Uncertain Hydroturbine Regulating System with Random Disturbances

    OpenAIRE

    Fengjiao Wu; Guitao Zhang; Zhengzhong Wang

    2016-01-01

    The robust fuzzy control for fractional-order hydroturbine regulating system is studied in this paper. First, the more practical fractional-order hydroturbine regulating system with uncertain parameters and random disturbances is presented. Then, on the basis of interval matrix theory and fractional-order stability theorem, a fuzzy control method is proposed for fractional-order hydroturbine regulating system, and the stability condition is expressed as a group of linear matrix inequalities. ...

  6. The Motion Path Study of Measuring Robot Based on Variable Universe Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Ma Guoqing

    2017-01-01

    Full Text Available For the problem of measuring robot requires a higher positioning, firstly learning about the error overview of the system, analysised the influence of attitude, speed and other factors on systematic errors. Then collected and analyzed the systematic error curve in the track to complete the planning process. The last adding fuzzy control in both cases, by comparing with the original system, can found that the method based on fuzzy control system can significantly reduce the error during the motion.

  7. 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.

  8. Energy Analysis for Air Conditioning System Using Fuzzy Logic Control

    Directory of Open Access Journals (Sweden)

    Henry Nasution

    2011-04-01

    Full Text Available Reducing energy consumption and to ensure thermal comfort are two important considerations for the designing an air conditioning system. An alternative approach to reduce energy consumption proposed in this study is to use a variable speed compressor. The control strategy will be proposed using the fuzzy logic controller (FLC. FLC was developed to imitate the performance of human expert operators by encoding their knowledge in the form of linguistic rules. The system is installed on a thermal environmental room with a data acquisition system to monitor the temperature of the room, coefficient of performance (COP, energy consumption and energy saving. The measurements taken during the two hour experimental periods at 5-minutes interval times for temperature setpoints of 20oC, 22oC and 24oC with internal heat loads 0, 500, 700 and 1000 W. The experimental results indicate that the proposed technique can save energy in comparison with On/Off and proportional-integral-derivative (PID control.

  9. 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.

  10. 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.

  11. Using fuzzy logic for automatic control: Case study of a problem of cereals samples classification

    Directory of Open Access Journals (Sweden)

    Lakhoua Najeh Mohamed

    2009-01-01

    Full Text Available The aim of this paper is to present the use of fuzzy logic for automatic control of industrial systems particularly the way to approach a problem of classification. We present a case study of a grading system of cereals that allows us to determine the price of transactions of cereals in Tunisia. Our contribution in this work consists in proposing not only an application of the fuzzy logic on the grading system of cereals but also a methodology enabling the proposing of a new grading system based on the concept of 'Grade' while using the fuzzy logic techniques. .

  12. Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Erdal Kayacan

    2017-01-01

    Full Text Available A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN is used in parallel with a conventional P (proportional controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs. The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

  13. 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

  14. Development of fuzzy algorithm with learning function for nuclear steam generator level control

    International Nuclear Information System (INIS)

    Park, Gee Yong; Seong, Poong Hyun

    1993-01-01

    A fuzzy algorithm with learning function is applied to the steam generator level control of nuclear power plant. This algorithm can make its rule base and membership functions suited for steam generator level control by use of the data obtained from the control actions of a skilled operator or of other controllers (i.e., PID controller). The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0 % - 30 % of full power) and the other to level control at high power level (30 % - 100 % of full power). Response time of steam generator level control at low power range with this rule base is shown to be shorter than that of fuzzy controller with direct inference. (Author)

  15. Discrete-time nonlinear sliding mode controller

    African Journals Online (AJOL)

    user

    Keywords: Discrete-time delay system, Sliding mode control, nonlinear sliding ... of engineering systems such as chemical process control, delay in the actuator ...... instrumentation from Motilal Nehru National Institute of Technology (MNNIT),.

  16. 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.

  17. 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.

  18. Design and implementation of fuzzy-PD controller based on relation models: A cross-entropy optimization approach

    Science.gov (United States)

    Anisimov, D. N.; Dang, Thai Son; Banerjee, Santo; Mai, The Anh

    2017-07-01

    In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.

  19. 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.

  20. SOS based robust H(∞) fuzzy dynamic output feedback control of nonlinear networked control systems.

    Science.gov (United States)

    Chae, Seunghwan; Nguang, Sing Kiong

    2014-07-01

    In this paper, a methodology for designing a fuzzy dynamic output feedback controller for discrete-time nonlinear networked control systems is presented where the nonlinear plant is modelled by a Takagi-Sugeno fuzzy model and the network-induced delays by a finite state Markov process. The transition probability matrix for the Markov process is allowed to be partially known, providing a more practical consideration of the real world. Furthermore, the fuzzy controller's membership functions and premise variables are not assumed to be the same as the plant's membership functions and premise variables, that is, the proposed approach can handle the case, when the premise of the plant are not measurable or delayed. The membership functions of the plant and the controller are approximated as polynomial functions, then incorporated into the controller design. Sufficient conditions for the existence of the controller are derived in terms of sum of square inequalities, which are then solved by YALMIP. Finally, a numerical example is used to demonstrate the validity of the proposed methodology.

  1. 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.

  2. Chaos control using sliding-mode theory

    International Nuclear Information System (INIS)

    Nazzal, Jamal M.; Natsheh, Ammar N.

    2007-01-01

    Chaos control means to design a controller that is able to mitigating or eliminating the chaos behavior of nonlinear systems that experiencing such phenomenon. In this paper, a nonlinear Sliding-Mode Controller (SMC) is presented. Two nonlinear chaotic systems are chosen to be our case study in this paper, the well known Chua's circuit and Lorenz system. The study shows the effectiveness of the designed nonlinear Sliding-Mode Controller

  3. Adaptive Sliding Mode Control Method Based on Nonlinear Integral Sliding Surface for Agricultural Vehicle Steering Control

    Directory of Open Access Journals (Sweden)

    Taochang Li

    2014-01-01

    Full Text Available Automatic steering control is the key factor and essential condition in the realization of the automatic navigation control of agricultural vehicles. In order to get satisfactory steering control performance, an adaptive sliding mode control method based on a nonlinear integral sliding surface is proposed in this paper for agricultural vehicle steering control. First, the vehicle steering system is modeled as a second-order mathematic model; the system uncertainties and unmodeled dynamics as well as the external disturbances are regarded as the equivalent disturbances satisfying a certain boundary. Second, a transient process of the desired system response is constructed in each navigation control period. Based on the transient process, a nonlinear integral sliding surface is designed. Then the corresponding sliding mode control law is proposed to guarantee the fast response characteristics with no overshoot in the closed-loop steering control system. Meanwhile, the switching gain of sliding mode control is adaptively adjusted to alleviate the control input chattering by using the fuzzy control method. Finally, the effectiveness and the superiority of the proposed method are verified by a series of simulation and actual steering control experiments.

  4. 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)

  5. A fuzzy development for attribute control chart with Monte Carlo simulation method

    Directory of Open Access Journals (Sweden)

    Mohammad Hadi Madadi

    2017-11-01

    Full Text Available This paper presents the case study of fuzzy statistical process control which has been simulated for variable and discontinuous production within a particular time frame in a key manufacturing work-shop. In order to reduce waste production and increase productivity, dimensional inspection from raw product is categorized into three groups: product of type A, product of type B, and discard. In first part, the appearance characteristics of product is defined as fuzzy membership function as the input of the system in order to allocate the output obtained from fuzzy inference of product to one of the three quality levels. Afterwards, each quality level is assigned to its own group by means of Monte Carlo simulation techniques. In the second part, with fuzzy development of a multinomial p chart, the production process is illustrated as a control chart within the particular period of time.

  6. 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.

  7. Image-based Fuzzy Parking Control of a Car-like Mobile Robot

    Directory of Open Access Journals (Sweden)

    Yin Yin Aye

    2017-03-01

    Full Text Available This paper develops a novel automatic parking system using an image-based fuzzy controller, where in the reasoning the slope and intercept of the desired target line are used for the inputs, and the steering angle of the robot is generated for the output. The objective of this study is that a robot equipped with a camera detects a rectangular parking frame, which is drawn on the floor, based on image processing. The desired target line to be followed by the robot is generated by using Hough transform from a captured image. The fuzzy controller is designed according to experiments of skilled driver, and the fuzzy rules are tuned and the fuzzy membership functions are optimized by experimentally for output. The effectiveness of the proposed method is demonstrated through some experimental results with an actual mobile robot

  8. 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.

  9. 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...

  10. Fuel Saving Strategy in Spark Ignition Engine Using Fuzzy Logic Engine Torque Control

    OpenAIRE

    Aris Triwiyatno; Sumardi

    2012-01-01

    In the case of injection gasoline engine, or better known as spark ignition engines, an effort to improve engine performance as well as to reduce fuel consumption is a fairly complex problem. Generally, engine performance improvement efforts will lead to increase in fuel consumption. However, this problem can be solved by implementing engine torque control based on intelligent regulation such as the fuzzy logic inference system. In this study, fuzzy logic engine torque regulation is used to c...

  11. Stabilizing periodic orbits of chaotic systems using fuzzy control of Poincare map

    International Nuclear Information System (INIS)

    Bonakdar, Mohammad; Samadi, Mostafa; Salarieh, Hassan; Alasty, Aria

    2008-01-01

    In this paper a fuzzy control algorithm is used to stabilize the fixed points of a chaotic system. No knowledge of the dynamic equations of the system is needed in this approach and the whole system is considered as a black box. Two main approaches have been investigated: fuzzy clustering and table look up methods. As illustrative examples these methods have been applied to Bonhoeffer van der Pol oscillator and the Henon chaotic system and the convergence toward fixed points is observed

  12. Stabilizing periodic orbits of chaotic systems using fuzzy control of Poincare map

    Energy Technology Data Exchange (ETDEWEB)

    Bonakdar, Mohammad; Samadi, Mostafa [Center of Excellence in Design, Robotics and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, 1458889694 Tehran (Iran, Islamic Republic of); Salarieh, Hassan [Center of Excellence in Design, Robotics and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, 1458889694 Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Center of Excellence in Design, Robotics and Automation (CEDRA), Department of Mechanical Engineering, Sharif University of Technology, Azadi Avenue, 1458889694 Tehran (Iran, Islamic Republic of)

    2008-05-15

    In this paper a fuzzy control algorithm is used to stabilize the fixed points of a chaotic system. No knowledge of the dynamic equations of the system is needed in this approach and the whole system is considered as a black box. Two main approaches have been investigated: fuzzy clustering and table look up methods. As illustrative examples these methods have been applied to Bonhoeffer van der Pol oscillator and the Henon chaotic system and the convergence toward fixed points is observed.

  13. Practical considerations for the implantation of a fuzzy control algorithm in a DSP

    International Nuclear Information System (INIS)

    Perez C, B.; Benitez R, J.S.; Pacheco S, J.O.

    2003-01-01

    The development of a digital system based on a DSP to implant a Mamdani type algorithm of fuzzy control whose objective is to regulate the neutron power in a nuclear research reactor Type TRIGA Mark III is presented. Its are simultaneously carried out the aggregation des fuzzy stages discreeting the universe of the output variable. The format MPF for the handling of the floating point in the arithmetic operations is used. (Author)

  14. 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.

  15. Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

    Directory of Open Access Journals (Sweden)

    Stephan Birle

    2016-01-01

    Full Text Available In food industry, bioprocesses like fermentation often are a crucial part of the manufacturing process and decisive for the final product quality. In general, they are characterized by highly nonlinear dynamics and uncertainties that make it difficult to control these processes by the use of traditional control techniques. In this context, fuzzy logic controllers offer quite a straightforward way to control processes that are affected by nonlinear behavior and uncertain process knowledge. However, in order to maintain process safety and product quality it is necessary to specify the controller performance and to tune the controller parameters. In this work, an approach is presented to establish an intelligent control system for oxidoreductive yeast propagation as a representative process biased by the aforementioned uncertainties. The presented approach is based on statistical process control and fuzzy logic feedback control. As the cognitive uncertainty among different experts about the limits that define the control performance as still acceptable may differ a lot, a data-driven design method is performed. Based upon a historic data pool statistical process corridors are derived for the controller inputs control error and change in control error. This approach follows the hypothesis that if the control performance criteria stay within predefined statistical boundaries, the final process state meets the required quality definition. In order to keep the process on its optimal growth trajectory (model based reference trajectory a fuzzy logic controller is used that alternates the process temperature. Additionally, in order to stay within the process corridors, a genetic algorithm was applied to tune the input and output fuzzy sets of a preliminarily parameterized fuzzy controller. The presented experimental results show that the genetic tuned fuzzy controller is able to keep the process within its allowed limits. The average absolute error to the

  16. High-Precision Control of a Piezo-Driven Nanopositioner Using Fuzzy Logic Controllers

    Directory of Open Access Journals (Sweden)

    Mohammed Altaher

    2018-01-01

    Full Text Available This paper presents single- and dual-loop fuzzy control schemes to precisely control the piezo-driven nanopositioner in the x- and y-axis directions. Various issues are associated with this control problem, such as low stability margin due to the sharp resonant peak, nonlinear dynamics, parameter uncertainty, etc. As such, damping controllers are often utilised to damp the mechanical resonance of the nanopositioners. The Integral Resonant Controller (IRC is used in this paper as a damping controller to damp the mechanical resonance. A further inherent problem is the hysteresis phenomenon (disturbance, which leads to degrading the positioning performance (accuracy of the piezo-driven stage. The common approach to treat this disturbance is to invoke tracking controllers in a closed-loop feedback scheme in conjunction with the damping controllers. The traditional approach uses the Integral Controller (I or Proportional Integral (PI as a tracking controller, whereas this paper introduces the Proportional and Integral (PI-like Fuzzy Logic Controller (FLC as a tracking controller. The effectiveness of the proposed control schemes over conventional schemes is confirmed through comparative simulation studies, and results are presented. The stability boundaries of the proposed control schemes are determined in the same way as with a conventional controller. Robustness against variations in the resonant frequency of the proposed control schemes is verified.

  17. Fuzzy Traffic Control with Vehicle-to-Everything Communication.

    Science.gov (United States)

    Salman, Muntaser A; Ozdemir, Suat; Celebi, Fatih V

    2018-01-27

    Traffic signal control (TSC) with vehicle-to everything (V2X) communication can be a very efficient solution to traffic congestion problem. Ratio of vehicles equipped with V2X communication capability in the traffic to the total number of vehicles (called penetration rate PR) is still low, thus V2X based TSC systems need to be supported by some other mechanisms. PR is the major factor that affects the quality of TSC process along with the evaluation interval. Quality of the TSC in each direction is a function of overall TSC quality of an intersection. Hence, quality evaluation of each direction should follow the evaluation of the overall intersection. Computational intelligence, more specifically swarm algorithm, has been recently used in this field in a European Framework Program FP7 supported project called COLOMBO. In this paper, using COLOMBO framework, further investigations have been done and two new methodologies using simple and fuzzy logic have been proposed. To evaluate the performance of our proposed methods, a comparison with COLOMBOs approach has been realized. The results reveal that TSC problem can be solved as a logical problem rather than an optimization problem. Performance of the proposed approaches is good enough to be suggested for future work under realistic scenarios even under low PR.

  18. DFCL: DYNAMIC FUZZY LOGIC CONTROLLER FOR INTRUSION DETECTION

    Directory of Open Access Journals (Sweden)

    Abdulrahim Haroun Ali

    2014-08-01

    Full Text Available Intrusions are a problem with the deployment of Networks which give misuse and abnormal behavior in running reliable network operations and services. In this work, a Dynamic Fuzzy Logic Controller (DFLC is proposed for an anomaly detection problem, with the aim of solving the problem of attack detection rate and faster response process. Data is collected by PingER project. PingER project actively measures the worldwide Internet’s end-to-end performance. It covers over 168 countries around the world. PingER uses simple ubiquitous Internet Ping facility to calculate number of useful performance parameters. From each set of 10 pings between a monitoring host and a remote host, the features being calculated include Minimum Round Trip Time (RTT, Jitter, Packet loss, Mean Opinion Score (MOS, Directness of Connection (Alpha, Throughput, ping unpredictability and ping reachability. A set of 10 pings is being sent from the monitoring node to the remote node every 30 minutes. The received data shows the current characteristic and behavior of the networks. Any changes in the received data signify the existence of potential threat or abnormal behavior. D-FLC uses the combination of parameters as an input to detect the existence of any abnormal behavior of the network. The proposed system is simulated in Matlab Simulink environment. Simulations results show that the system managed to catch 95% of the anomalies with the ability to distinguish normal and abnormal behavior of the network.

  19. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Kim, Dong Yun; Seong, Poong Hyun

    1996-01-01

    In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller

  20. Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

    International Nuclear Information System (INIS)

    Kim, Dong Yun

    1997-02-01

    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 adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with 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