WorldWideScience

Sample records for adaptive fuzzy controller

  1. Efficient adaptive fuzzy control scheme

    NARCIS (Netherlands)

    Papp, Z.; Driessen, B.J.F.

    1995-01-01

    The paper presents an adaptive nonlinear (state-) feedback control structure, where the nonlinearities are implemented as smooth fuzzy mappings defined as rule sets. The fine tuning and adaption of the controller is realized by an indirect adaptive scheme, which modifies the parameters of the fuzzy

  2. Adaptive Fuzzy Control for CVT Vehicle

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    On the simple continuously variable transmission (CVT) driveline model, the design of adaptive fuzzy control system for CVT vehicle is presented. The adaptive fuzzy control system consists of a scaling factor self-tuning fuzzy-PI throttle controller, and a hybrid fuzzy-PID CVT ratio and brake controller. The presented adaptive fuzzy control strategy is vehicle model independent, which depends only on the instantaneous vehicle states, but does not depend on vehicle parameters. So it has good robustness against uncertain vehicle parameters and exogenous load disturbance. Simulation results show that the proposed adaptive fuzzy strategy has good adaptability and practicality value.

  3. INDIRECT ACCELERATED ADAPTIVE FUZZY CONTROLLER

    Institute of Scientific and Technical Information of China (English)

    ZHU Liye; FANG Yuan; ZHANG Weidong

    2008-01-01

    According to a type of normal nonlinear system, an indirect adaptive fuzzy (IAF) controller has been applied to those systems where no accurate mathematical models of the systems under control are available. To satisfy with system performance, an indirect accelerated adaptive fuzzy (IAAF) controller is proposed, and its general form is presented. The general form IAAF controller ensures necessary control criteria and system's global stability using Lyapunov Theorem. It has been proved that the close-loop system error converges to a small neighborhood of equilibrium point. The optimal IAAF controller is derived to guarantee the process's shortest settling time. Simulation results indicate the IAAF controller make the system more stable, accurate, and fast.

  4. Adaptive fuzzy controllers based on variable universe

    Institute of Scientific and Technical Information of China (English)

    李洪兴

    1999-01-01

    Adaptive fuzzy controllers by means of variable universe are proposed based on interpolation forms of fuzzy control. First, monotonicity of control rules is defined, and it is proved that the monotonicity of interpolation functions of fuzzy control is equivalent to the monotonicity of control rules. This means that there is not any contradiction among the control rules under the condition for the control rules being monotonic. Then structure of the contraction-expansion factor is discussed. At last, three models of adaptive fuzzy control based on variable universe are given which are adaptive fuzzy control model with potential heredity, adaptive fuzzy control model with obvious heredity and adaptive fuzzy control model with successively obvious heredity.

  5. Application of Adaptive Fuzzy PID Leveling Controller

    Directory of Open Access Journals (Sweden)

    Ke Zhang

    2013-05-01

    Full Text Available Aiming at the levelling precision, speed and stability of suspended access platform, this paper put forward a new adaptive fuzzy PID control levelling algorithm by fuzzy theory. The method is aided design by using the SIMULINK toolbox of MATLAB, and setting the membership function and the fuzzy-PID control rule. The levelling algorithm can real-time adjust the three parameters of PID according to the fuzzy rules due to the current state. It is experimented, which is verified the algorithm have better stability and dynamic performance.

  6. Adaptive Fuzzy Attitude Control of Flexible Satellite

    Institute of Scientific and Technical Information of China (English)

    GUAN Ping; LIU Xiang-dong; CHEN Jia-bin

    2005-01-01

    The adaptive fuzzy control is applied in the attitude stabilization of flexible satellite. The detailed design procedure of the adaptive fuzzy control system is presented. Two T-S models are used as both controller and identifier. The parameters of the controller could be modified according to the information of the identifier. Simulation results show that the method can effectively cope with the uncertainty of flexible satellite by on-line learning and thus posses the good robustness. With the proposed method, the precise attitude control is accomplished.

  7. A Novel Robust Adaptive Fuzzy Controller

    Institute of Scientific and Technical Information of China (English)

    LIU Xiao-hua; WANG Xiu-hong; FEN En-min

    2002-01-01

    For a class of continuous-time nonlinear system, a novel robust adaptive fuzzy controller is proposed by using of Lyapunov method. It is proven that the control algorithm is globally stable, the output tracking-error can convergence to a domain of zero under the assumptions. As a result, the system controlled has stronger robustness for disturbance and modeling error.

  8. Decentralized adaptive fuzzy control of robot manipulators.

    Science.gov (United States)

    Jin, Y

    1998-01-01

    This paper develops a decentralized adaptive fuzzy control scheme for robot manipulators via a combination of genetic algorithm and gradient method. The controller for each link consists of a feedforward fuzzy torque-computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line by an improved genetic algorithm, that is to say, not only the parameters but also the structure of the fuzzy system are self-organized. Because genetic algorithm can operate successfully without the system model, no exact inverse dynamics of the robot system are required. The feedback fuzzy PD system, on the other hand, is tuned on-line using gradient method. In this way, the proportional and derivative gains are adjusted properly to keep the closed-loop system stable. The proposed controller has the following merits: (1) it needs no exact dynamics of the robot systems and the computation is time-saving because of the simple structure of the fuzzy systems; and (2) the controller is insensitive to various dynamics and payload uncertainties in robot systems. These are demonstrated by analyses of the computational complexity and various computer simulations.

  9. Robust adaptive control of MEMS triaxial gyroscope using fuzzy compensator.

    Science.gov (United States)

    Fei, Juntao; Zhou, Jian

    2012-12-01

    In this paper, a robust adaptive control strategy using a fuzzy compensator for MEMS triaxial gyroscope, which has system nonlinearities, including model uncertainties and external disturbances, is proposed. A fuzzy logic controller that could compensate for the model uncertainties and external disturbances is incorporated into the adaptive control scheme in the Lyapunov framework. The proposed adaptive fuzzy controller can guarantee the convergence and asymptotical stability of the closed-loop system. The proposed adaptive fuzzy control strategy does not depend on accurate mathematical models, which simplifies the design procedure. The innovative development of intelligent control methods incorporated with conventional control for the MEMS gyroscope is derived with the strict theoretical proof of the Lyapunov stability. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive fuzzy control scheme and demonstrate the satisfactory tracking performance and robustness against model uncertainties and external disturbances compared with conventional adaptive control method.

  10. Decomposed fuzzy systems and their application in direct adaptive fuzzy control.

    Science.gov (United States)

    Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang

    2014-10-01

    In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.

  11. SOFC temperature evaluation based on an adaptive fuzzy controller

    Institute of Scientific and Technical Information of China (English)

    Xiao-juan WU; Xin-jian ZHU; Guang-yi CAO; Heng-yong TU

    2008-01-01

    The operating temperature of a solid oxide fuel cell (SOFC) stack is a very important parameter to be controlled, which impacts the performance of the SOFC due to thermal cycling. In this paper, an adaptive fuzzy control method based on an affine nonlinear temperature model is developed to control the temperature of the SOFC within a specified range. Fuzzy logic systems are used to approximate nonlinear functions in the SOFC system and an adaptive technique is employed to construct the controller. Compared with the traditional fuzzy and proportion-integral-derivative (PID) control, the simulation results show that the designed adaptive fuzzy control method performed much better. So it is feasible to build an adaptive fuzzy controller for temperature control of the SOFC.

  12. Variable universe adaptive fuzzy control on the quadruple inverted pendulum

    Institute of Scientific and Technical Information of China (English)

    LI; Hongxing(

    2002-01-01

    [1]Magana,M.E.,Fuzzy-logic control of an inverted pendulum with vision feedback,IEEE Transactions on Education,1998,41(2):165.[2]Chen,C.S.,Chen,W.L.,Robust adaptive sliding-mode control using fuzzy modeling for an inverted-pendulum system,IEEE Transactions on Industrial Electronics,1998,45(2):297.[3]Cheng,F.Y.,Zhong,G.M.,Li,Y.S.et al.,Fuzzy control of a double-inverted pendulum,Fuzzy Sets and System,1996,79(3):315-321.[4]Zhang,H.M.,Ma,X.W.,Xu,W.et al.,Design fuzzy controllers complex systems with an application to 3-stage inverted pendulums,Information Sciences,1993,72:271.[5]Zhang,M.L.,Hao,J.K.,Hei,W.D.,Personification intelligence control and triple inverted pendulum,Journal of Aeronautics (in Chinese),1995,16(4):654.[6]Li,H.X.,To see the success of fuzzy logic from mathematical essence of fuzzy control,Fuzzy Systems and Mathematics (in Chinese),1995,9(4):1-14.[7]Li,H.X.,Mathematical essence of fuzzy controls and design of a kind of high precision fuzzy controllers,Control Theory and Application (in Chinese),1997,14(6):868.[8]Li,H.X.,Adaptive fuzzy controllers based on variable universe,Science in China,Ser.E,1999,42(1):10.[9]Li,H.X.,Interpolation mechanism of fuzzy control,Science in China,Ser.E,1998,41(3):312.[10]Li,H.X.,The equivalence between fuzzy logic systems and feedforward neural networks,Science in China,Ser.E,2000,43(1):42.

  13. Optimal Power Flow Using Adaptive Fuzzy Logic Controllers

    Directory of Open Access Journals (Sweden)

    Abdullah M. Abusorrah

    2013-01-01

    Full Text Available This paper presents an approach for optimum reactive power dispatch through the power network with flexible AC transmission systems (FACTSs devices, using adaptive fuzzy logic controller (AFLC driven by adaptive fuzzy sets (AFSs. The membership functions of AFLC are optimized based on 2nd-order fuzzy set specifications. The operation of FACTS devices (particularly, static VAR compensator (SVC and the setting of their control parameters (QSVC are optimized dynamically based on the proposed AFLC to enhance the power system stability in addition to their main function of power flow control. The proposed AFLC is compared with a static fuzzy logic controller (SFLC, driven by a fixed fuzzy set (FFS. Simulation studies were carried out and validated on the standard IEEE 30-bus test system.

  14. Variable universe stable adaptive fuzzy control of nonlinear system

    Institute of Scientific and Technical Information of China (English)

    李洪兴; 苗志宏; 王加银

    2002-01-01

    A kind of stable adaptive fuzzy control of nonlinear system is implemented using variable universe method. First of all, the basic structure of variable universe adaptive fuzzy controllers is briefly introduced. Then the contraction-expansion factor that is a key tool of variable universe method is defined by means of integral regulation idea, and a kind of adaptive fuzzy controllers is designed by using such a contraction-expansion factor. The simulation on first order nonlinear system is done. Secondly, it is proved that the variable universe adaptive fuzzy control is asymptotically stable by use of Lyapunov theory. The simulation on the second order nonlinear system shows that its simulation effect is also quite good. Finally a useful tool, called symbolic factor, is proposed, which may be of universal significance. It can greatly reduce the settling time and enhance the robustness of the system.

  15. Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

    Institute of Scientific and Technical Information of China (English)

    Mingjun ZHANG; Huaguang ZHANG

    2006-01-01

    In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model (GFHM). Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.

  16. Adaptive Fuzzy Knowledge Based Controller for Autonomous Robot Motion Control

    Directory of Open Access Journals (Sweden)

    Mbaitiga Zacharie

    2010-01-01

    Full Text Available Problem statement: Research into robot motion control offers research opportunities that will change scientists and engineers for year to come. Autonomous robots are increasingly evident in many aspects of industry and everyday life and a robust robot motion control can be used for homeland security and many consumer applications. This study discussed the adaptive fuzzy knowledge based controller for robot motion control in indoor and outdoor environment. Approach: The proposed method consisted of two components: the process monitor that detects changes in the process characteristics and the adaptation mechanism that used information passed to it by the process monitor to update the controller parameters. Results: Experimental evaluation had been done in both indoor and outdoor environment where the robot communicates with the base station through its Wireless fidelity antenna and the performance monitor used a set of five performance criteria to access the fuzzy knowledge based controller. Conclusion: The proposed method had been found to be robust.

  17. 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.

  18. 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.

  19. 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....

  20. 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....

  1. Enhanced adaptive fuzzy sliding mode control for uncertain nonlinear systems

    Science.gov (United States)

    Roopaei, Mehdi; Zolghadri, Mansoor; Meshksar, Sina

    2009-09-01

    In this article, a novel Adaptive Fuzzy Sliding Mode Control (AFSMC) methodology is proposed based on the integration of Sliding Mode Control (SMC) and Adaptive Fuzzy Control (AFC). Making use of the SMC design framework, we propose two fuzzy systems to be used as reaching and equivalent parts of the SMC. In this way, we make use of the fuzzy logic to handle uncertainty/disturbance in the design of the equivalent part and provide a chattering free control for the design of the reaching part. To construct the equivalent control law, an adaptive fuzzy inference engine is used to approximate the unknown parts of the system. To get rid of the chattering, a fuzzy logic model is assigned for reaching control law, which acting like the saturation function technique. The main advantage of our proposed methodology is that the structure of the system is unknown and no knowledge of the bounds of parameters, uncertainties and external disturbance are required in advance. Using Lyapunov stability theory and Barbalat's lemma, the closed-loop system is proved to be stable and convergence properties of the system is assured. Simulation examples are presented to verify the effectiveness of the method. Results are compared with some other methods proposed in the past research.

  2. Adaptive Fuzzy Dynamic Surface Control for Uncertain Nonlinear Systems

    Institute of Scientific and Technical Information of China (English)

    Xiao-Yuan Luo; Zhi-Hao Zhu; Xin-Ping Guan

    2009-01-01

    In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of "explosion of complexity" in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globaily uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller (SMC). Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.

  3. Variable universe adaptive fuzzy control on the quadruple inverted pendulum

    Institute of Scientific and Technical Information of China (English)

    李洪兴; 苗志宏; 王家银

    2002-01-01

    This paper focuses on the control problem of the quadruple inverted pendulum by variable universe adaptive fuzzy control.First,the mathematical model on the quadruple inverted pendulum is described and its controllability is versified.Then,an efficient controller on the quadruple inverted pendulum is designed by using variable universe adaptive fuzzy control theory.Finally the simulation of the quadruple inverted pendulum is shown in detail.Besides,the experimental results on the hardware systems,i.e.real object systems,on a single inverted pendulum,a double inverted pendulum and a triple inverted pendulum are briefly introduced.``

  4. Indirect Adaptive Fuzzy and Impulsive Control of Nonlinear Systems

    Institute of Scientific and Technical Information of China (English)

    Hai-Bo Jiang

    2010-01-01

    The problem of indirect adaptive fuzzy and impulsive control for a class of nonlinear systems is investigated.Based on the approximation capability of fuzzy systems,a novel adaptive fuzzy and impulsive control strategy with supervisory controller is developed.With the help of a supervisory controller,global stability of the resulting closed-loop system is established in the sense that all signals involved are uniformly bounded.Furthermore,the adaptive compensation term of the upper bound function of the sum of residual and approximation error is adopted to reduce the effects of modeling error.By the generalized Barbalat's lemma,the tracking error between the output of the system and the reference signal is proved to be convergent to zero asymptotically.Simulation results illustrate the effectiveness of the proposed approach.

  5. Adaptive Fuzzy Control for Nonstrict Feedback Systems With Unmodeled Dynamics and Fuzzy Dead Zone via Output Feedback.

    Science.gov (United States)

    Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan

    2017-09-01

    This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

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

    CERN Document Server

    Xue Yue Ju

    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.

  7. Flexible Satellite Attitude Control via Adaptive Fuzzy Linearization

    Institute of Scientific and Technical Information of China (English)

    GUAN Ping; LIU Xiang-dong; CHEN Jia-bin; LIU Xiao-he

    2005-01-01

    The adaptive fuzzy control is combined with input-output linearization control to constitute the hybrid controller. The control method is then applied to the attitude maneuver control of the flexible satellite.The basic control structure is given. The rules of the controller parameter selection, which guarantee the attitude stabilization of the satellite with parameter uncertainties, have been analyzed. Simulation results show that the precise attitude control is accomplished in spite of the uncertainty in the system.

  8. Adaptive Fuzzy and Robust H∞ Compensation Control for Uncertain Robot

    Directory of Open Access Journals (Sweden)

    Yuan Chen

    2013-06-01

    Full Text Available In this paper, two types of robust adaptive compensation control schemes for the trajectory tracking control of robot manipulator with uncertain dynamics are proposed. The proposed controllers incorporate the computed-torque control scheme as a nominal portion of the controller; an adaptive fuzzy control algorithm to approximate the structured uncertainties; and a nonlinear H∞ tracking control model as a feedback portion to eliminate the effects of the unstructured uncertainties and approximation errors. The validity of the robust adaptive compensation control schemes is investigated by numerical simulations of a two-link rotary robot manipulator

  9. Blowdown wind tunnel control using an adaptive fuzzy PI controller

    Directory of Open Access Journals (Sweden)

    Corneliu Andrei NAE

    2013-09-01

    Full Text Available The paper presents an approach towards the control of a supersonic blowdown wind tunnel plant (as evidenced by experimental data collected from “INCAS Supersonic Blowdown Wind Tunnel” using a PI type controller. The key to maintain the imposed experimental conditions is the control of the air flow using the control valve of the plant. A proposed mathematical model based on the control valve will be analyzed using the PI controller. This control scheme will be validated using experimental data collected from real test cases. In order to improve the control performances an adaptive fuzzy PI controller will be implemented in SIMULINK in the present paper. The major objective is to reduce the transient regimes and the global reduction of the start-up loads on the models during this phase.

  10. Nonlinear adaptive control based on fuzzy sliding mode technique and fuzzy-based compensator.

    Science.gov (United States)

    Nguyen, Sy Dzung; Vo, Hoang Duy; Seo, Tae-Il

    2017-09-01

    It is difficult to efficiently control nonlinear systems in the presence of uncertainty and disturbance (UAD). One of the main reasons derives from the negative impact of the unknown features of UAD as well as the response delay of the control system on the accuracy rate in the real time of the control signal. In order to deal with this, we propose a new controller named CO-FSMC for a class of nonlinear control systems subjected to UAD, which is constituted of a fuzzy sliding mode controller (FSMC) and a fuzzy-based compensator (CO). Firstly, the FSMC and CO are designed independently, and then an adaptive fuzzy structure is discovered to combine them. Solutions for avoiding the singular cases of the fuzzy-based function approximation and reducing the calculating cost are proposed. Based on the solutions, fuzzy sliding mode technique, lumped disturbance observer and Lyapunov stability analysis, a closed-loop adaptive control law is formulated. Simulations along with a real application based on a semi-active train-car suspension are performed to fully evaluate the method. The obtained results reflected that vibration of the chassis mass is insensitive to UAD. Compared with the other fuzzy sliding mode control strategies, the CO-FSMC can provide the best control ability to reduce unwanted vibrations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Identification and adaptive control scheme using fuzzy parameterized linear filters

    NARCIS (Netherlands)

    Papp, Z.

    1998-01-01

    A nonlinear fuzzy control structure enhanced with supervised learning and/or adaption is presented. Availability of at least a partial process model is assumed. Nonlinear process identification procedure is used to complete the partial model. Based on the identification model the system sensitivity

  12. Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer

    Directory of Open Access Journals (Sweden)

    Dezhi Xu

    2013-01-01

    Full Text Available We propose a terminal sliding mode control (SMC law based on adaptive fuzzy-neural observer for nonaffine nonlinear uncertain system. First, a novel nonaffine nonlinear approximation algorithm is proposed for observer and controller design. Then, an adaptive fuzzy-neural observer is introduced to identify the simplified model and resolve the problem of the unavailability of the state variables. Moreover, based on the information of the adaptive observer, the terminal SMC law is designed. The Lyapunov synthesis approach is used to guarantee a global uniform ultimate boundedness property of the state estimation error and the asymptotic output tracking of the closed-loop control systems in spite of unknown uncertainties/disturbances, as well as all the other signals in the closed-loop system. Finally, using the designed terminal sliding mode controller, the simulation results on the dynamic model demonstrate the effectiveness of the proposed new control techniques.

  13. Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

    Science.gov (United States)

    Esogbue, Augustine O.

    1998-01-01

    The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of

  14. Fuzzy Adaptive PI Controller for DTFC in Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Medjdoub khessam

    2014-12-01

    Full Text Available This paper presents a technique to control the electric vehicle (EV speed and torque at any curve. Our propulsion model consist of two permanent magnet synchronous (PMSM motors. The fuzzy adaptive PI controller is used to adjust the different static error constants, as per the speed error. The suggested based on the direct torque fuzzy control (DTFC. A Mamdani type fuzzy direct torque controller is first developed and then rules are modified using stator current membership functions. The computations are ensured by the electronic differential, this driving process permit to steer each driving wheels at any curve separately.Modeling and simulation are carried out using the Matlab/Simulink tool to investigate the performance of the proposed system.

  15. Adaptive fuzzy sliding mode control of Lorenz chaotic system

    Institute of Scientific and Technical Information of China (English)

    WU Ligang; WANG Changhong

    2007-01-01

    By using the exponential reaching law technology,a sliding mode controller was designed for Lorenz chaotic system subject to an unknown external disturbance.On this basis,considering the unknown disturbance,an adaptive law was introduced to adaptively estimate the parameters of the disturbance bounds.Furthermore,to eliminate the chattering resulting from the discontinuous switch controller and to guarantee system transient performance,a new adaptive fuzzy sliding mode controller was designed.The results of the simulation show the effectiveness of the proposed control scheme.

  16. An adaptive fuzzy logic controller for robot-manipulator

    Directory of Open Access Journals (Sweden)

    Tran Thu Ha

    2008-11-01

    Full Text Available In this paper, an adaptive fuzzy controller is designed for the robot-manipulator. The synthesized controller ensures that 1 the close-loop system is globally stable and 2 the tracking error converges to zero asymptotically and a cost function is minimized. The fuzzy controller is synthesized from a collection of IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. The proposed control scheme is demonstrated in a typical nonlinear plant two link manipulator. The computer simulation of control is done by the language MATLAB. The results of simulation show that the adaptipresented results are analyzed.

  17. Fuzzy adaptive PID control for six rotor eppo UAV

    Directory of Open Access Journals (Sweden)

    Yongwei LI

    2017-02-01

    Full Text Available Six rotor eppo drones's load change itself in the job process will reduce the aircraft flight control performance and make the resistance to environmental disturbance being poor. In order to improve the six rotor eppo unmanned aerial vehicle (UAV control performance, the UAV in the process of spraying pesticide is analyzed and the model is constructed, then the eppo UAV time-varying dynamics mathematical model is deduced, and a fuzzy adaptive PID control algorithm is proposed. Fuzzy adaptive PID algorithm has good adaptability and the parameter setting is simple, which improves the system dynamic response and steady state performance, realizing the stability of the six rotor eppo UAV flight. With measured parameters of each sensor input in to the fuzzy adaptive PID algorithm, the corresponding control quality is obtained, and the stable operation of aircraft is realized. Through using Matlab to simulate the flight system and combining the practical experiments, it shows that the dynamic performance and stability of the system is improved effetively.

  18. Robust observer-based adaptive fuzzy sliding mode controller

    Science.gov (United States)

    Oveisi, Atta; Nestorović, Tamara

    2016-08-01

    In this paper, a new observer-based adaptive fuzzy integral sliding mode controller is proposed based on the Lyapunov stability theorem. The plant is subjected to a square-integrable disturbance and is assumed to have mismatch uncertainties both in state- and input-matrices. Based on the classical sliding mode controller, the equivalent control effort is obtained to satisfy the sufficient requirement of sliding mode controller and then the control law is modified to guarantee the reachability of the system trajectory to the sliding manifold. In order to relax the norm-bounded constrains on the control law and solve the chattering problem of sliding mode controller, a fuzzy logic inference mechanism is combined with the controller. An adaptive law is then introduced to tune the parameters of the fuzzy system on-line. Finally, for evaluating the controller and the robust performance of the closed-loop system, the proposed regulator is implemented on a real-time mechanical vibrating system.

  19. A Robot Manipulator with Adaptive Fuzzy Controller in Obstacle Avoidance

    Science.gov (United States)

    Sreekumar, Muthuswamy

    2016-07-01

    Building robots and machines to act within a fuzzy environment is a problem featuring complexity and ambiguity. In order to avoid obstacles, or move away from it, the robot has to perform functions such as obstacle identification, finding the location of the obstacle, its velocity, direction of movement, size, shape, and so on. This paper presents about the design, and implementation of an adaptive fuzzy controller designed for a 3 degree of freedom spherical coordinate robotic manipulator interfaced with a microcontroller and an ultrasonic sensor. Distance between the obstacle and the sensor and its time rate are considered as inputs to the controller and how the manipulator to take diversion from its planned trajectory, in order to avoid collision with the obstacle, is treated as output from the controller. The obstacles are identified as stationary or moving objects and accordingly adaptive self tuning is accomplished with three set of linguistic rules. The prototype of the manipulator has been fabricated and tested for collision avoidance by placing stationary and moving obstacles in its planned trajectory. The performance of the adaptive control algorithm is analyzed in MATLAB by generating 3D fuzzy control surfaces.

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

    OpenAIRE

    Junhai Luo; Heng Liu

    2014-01-01

    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 th...

  1. Robust direct adaptive fuzzy control for nonlinear MIMO systems

    Institute of Scientific and Technical Information of China (English)

    ZHANG Huaguang; ZHANG Mingjun

    2006-01-01

    For a class of nonlinear multi-input multi-output systems with uncertainty, a robust direct adaptive fuzzy control scheme was proposed. The feedback control law and adaptive law for parameters were derived based on Lyapunov design approach. The overall control scheme can guarantee that the tracking error converges in the small neighborhood of origin, and all signals of the closed-loop system are uniformly bounded. The main advantage of the proposed control scheme is that in each subsystem only one parameter vector needs to be adjusted on-line in the adaptive mechanism, and so the on-line computing burden is reduced. In addition, the proposed control scheme is a smooth control with no chattering phenomena. A simulation example was proposed to demonstrate the effectiveness of the proposed control algorithm.

  2. Adaptive process control using fuzzy logic and genetic algorithms

    Science.gov (United States)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

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

    Directory of Open Access Journals (Sweden)

    Junhai Luo

    2014-01-01

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

  4. Direct Adaptive Fuzzy Sliding Mode Control with Variable Universe Fuzzy Switching Term for a Class of MIMO Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Guo Haigang

    2012-01-01

    Full Text Available Combining adaptive fuzzy sliding mode control with fuzzy or variable universe fuzzy switching technique, this study develops two novel direct adaptive schemes for a class of MIMO nonlinear systems with uncertainties and external disturbances. The proposed control schemes consist of fuzzy equivalent control terms, fuzzy switching control terms (in scheme one or variable universe fuzzy switching control terms (in scheme two, and compensation control terms. The compensation control terms are used to relax the assumption on fuzzy approximation error. Based on Lyapunov stability theory, the parameters update laws are adaptively tuned online and the global asymptotic stability of the closed-loop system can be guaranteed. The major contribution of this study is to develop a novel framework for designing direct adaptive fuzzy sliding mode control scheme facing model uncertainties and external disturbances. The derived schemes can effectively solve the chattering problem and the equivalent control calculation in that environment. Simulation results performed on a two-link robotic manipulator demonstrate the feasibility of the proposed control schemes.

  5. Improved adaptive fuzzy control for MIMO nonlinear time-delay systems

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    This paper presents an improved observer-based indirect adaptive fuzzy control scheme for multiinput-multioutput (MIMO) nonlinear time-delay systems.The control scheme synthesizes adaptive fuzzy control with adaptive fuzzy identification.An observer is designed to observe the system state,and an identifier is developed to identify the unknown parts of the system.The update laws for parameters utilize two types of errors in the adaptive time-delay fuzzy logic systems,the observation error and the identificat...

  6. An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream

    Science.gov (United States)

    Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.

    2016-01-01

    This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081

  7. Small unmanned helicopter's attitude controller by an on-line adaptive fuzzy control system

    Institute of Scientific and Technical Information of China (English)

    GAO Tong-yue; RAO Jin-jun; GONG Zhen-bang; LUO Jun

    2009-01-01

    Since small unmanned helicopter flight attitude control process has strong time-varying characteristics and there are random disturbances, the conventional control methods with unchanged parameters are often unworkable. An on-line adaptive fuzzy control system (AFCS) was designed, in a way that does not depend on a process model of the plant or its approximation in the form of a Jacobian matrix. Neither is it necessary to know the desired response at each instant of time. AFCS implement a simultaneous on-line tuning of fuzzy rules and output scale of fuzzy control system. The two cascade controller design with an inner (attitude controller) and outer controller (navigation controller) of the small unmanned helicopter was proposed. At last, an attitude controller based on AFCS was implemented. The flight experiment showed that the proposed fuzzy logic controller provides quicker response, smaller overshoot, higher precision, robustness and adaptive ability. It satisfies the needs of autonomous flight.

  8. Adaptive Neuro-fuzzy Controller Design for Non-affine Nonlinear Systems

    Institute of Scientific and Technical Information of China (English)

    JIA Li; GE Shu-zhi; QIU Ming-sen

    2008-01-01

    An adaptive neuro-fuzzy control is investigated for a class of noa-affine nonlinear systems.To do so,rigorous description and quantification of the approximation error of the neuro-fuzzy controller are firstly discussed.Applying this result and Lyapunov stability theory,a novel updating algorithm to adapt the weights,centers,and widths of the neuro-fuzzy controller is presented.Consequently,the proposed design method is able to guaranteg the stability of the closed-loop system and the convergence of the tracking error.Simulation results illustrate the effectiveness of the proposed adaptive neuro-fuzzy control scheme.

  9. Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology

    Institute of Scientific and Technical Information of China (English)

    Qi Zhidong; Zhu Xinjian; Cao Guangyi

    2006-01-01

    Aiming at on-line controlling of Direct Methanol Fuel Cell (DMFC) stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System (ANFIS) identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative (PID) and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.

  10. Backstepping design of missile guidance and control based on adaptive fuzzy sliding mode control

    Institute of Scientific and Technical Information of China (English)

    Ran Maopeng; Wang Qing; Hou Delong; Dong Chaoyang

    2014-01-01

    This paper presents an integrated missile guidance and control law based on adaptive fuzzy sliding mode control. The integrated model is formulated as a block-strict-feedback nonlinear system, in which modeling errors, unmodeled nonlinearities, target maneuvers, etc. are viewed as unknown uncertainties. The adaptive nonlinear control law is designed based on backstepping and sliding mode control techniques. An adaptive fuzzy system is adopted to approximate the coupling nonlinear functions of the system, and for the uncertainties, we utilize an online-adaptive control law to estimate the unknown parameters. The stability analysis of the closed-loop system is also conducted. Simulation results show that, with the application of the adaptive fuzzy sliding mode control, small miss distances and smooth missile trajectories are achieved, and the system is robust against system uncertainties and external disturbances.

  11. Adaptive Current Control with PI-Fuzzy Compound Controller for Shunt Active Power Filter

    Directory of Open Access Journals (Sweden)

    Juntao Fei

    2013-01-01

    Full Text Available An adaptive control technology and PI-fuzzy compound control technology are proposed to control an active power filter (APF. AC side current compensation and DC capacitor voltage tracking control strategy are discussed and analyzed. Model reference adaptive controller for the AC side current compensation is derived and established based on Lyapunov stability theory; proportional and integral (PI fuzzy compound controller is designed for the DC side capacitor voltage control. The adaptive current controller based on PI-fuzzy compound system is compared with the conventional PI controller for active power filter. Simulation results demonstrate the feasibility and satisfactory performance of the proposed control strategies. It is shown that the proposed control method has an excellent dynamic performance such as small current tracking error, reduced total harmonic distortion (THD, and strong robustness in the presence of parameters variation and nonlinear load.

  12. An Adaptive Fuzzy Control Approach for the Robust Tracking of a MEMS Gyroscope Sensor

    Directory of Open Access Journals (Sweden)

    Juntao Fei

    2011-11-01

    Full Text Available In this paper, a direct adaptive fuzzy control using a supervisory compensator is designed for the robust tracking of a MEMS gyroscope sensor. The parameters of the membership functions are adjusted according to the designed adaptive law for the purpose of tracking a reference trajectory. A fuzzy controller that can approximate the unknown nonlinear function and compensate the system

  13. Fuzzy Adaptive Control for Trajectory Tracking of Autonomous Underwater Vehicle

    Directory of Open Access Journals (Sweden)

    Saeed Nakhkoob

    2014-01-01

    Full Text Available In this paper, the problem of the position and attitude tracking of an autonomous underwater vehicle (AUV in the horizontal plane, under the presence of ocean current disturbances is discussed. The effect of the gradual variation of the parameters is taken into account. The effectiveness of the adaptive controller is compared with a feedback linearization method and fuzzy gain control approach. The proposed strategy has been tested through simulations. Also, the performance of the propos-ed method is compared with other strategies given in some other studies. The boundedness and asymptotic converge-nce properties of the control algorithm and its semi-global stability are analytically proven using Lyapunov stability theory and Barbalat’s lemma.

  14. Design of Adaptive Fuzzy PID Altitude Control System for Unmanned Aerial Vehicle

    Institute of Scientific and Technical Information of China (English)

    SHI Gang; YANG Shu-xing; JING Ya-xing; XU Yong

    2008-01-01

    Based on Matlab/Simulink and Fuzzy Logic toolboxes, the altitude control system is designed and simulated. The validity of conventional PID control method and adaptive fuzzy PID control method is compared. It can be drawn out that the adaptive fuzzy PID control method is superior to the conventional PID in rising time and overshoot etc. The effectiveness of a fuzzy PID controller shows potential application in the future, especially in the presence of model uncertainty or changing dynamics and time-varying parameters.

  15. Fuzzy Adaptive Control System of a Non-Stationary Plant

    Science.gov (United States)

    Nadezhdin, Igor S.; Goryunov, Alexey G.; Manenti, Flavio

    2016-08-01

    This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.

  16. A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    OpenAIRE

    Abdul Kareem; Mohammad Fazle Azeem

    2012-01-01

    This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness ...

  17. Fuzzy adaptive learning control network with sigmoid membership function

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To get simpler operation in modified fuzzy adaptive learning control network (FALCON) in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing (SA) learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived;and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

  18. Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.

    Science.gov (United States)

    Wang, Wei; Tong, Shaocheng

    2017-01-10

    This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper.

  19. 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.

  20. Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Shao-Cheng Tong; Yong-Ming Li

    2009-01-01

    In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy-neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed rccursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

  1. 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.

  2. Fuzzy Adaptive Repetitive Control for Periodic Disturbance with Its Application to High Performance Permanent Magnet Synchronous Motor Speed Servo Systems

    National Research Council Canada - National Science Library

    Junxiao Wang

    2016-01-01

    .... Then, the mathematical model of PMSM is given. Subsequently, a fuzzy adaptive repetitive controller based on repetitive control and fuzzy logic control is designed for the PMSM speed servo system...

  3. 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.

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

    Directory of Open Access Journals (Sweden)

    Li Jing

    2016-01-01

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

  5. Adaptive Fuzzy Logic Control of Wind Turbine Emulator

    Directory of Open Access Journals (Sweden)

    BOUZID Mohamed Amine

    2014-03-01

    Full Text Available In this paper, a Wind Turbine Emulator (WTE based on a separately excited direct current (DC motor is studied. The wind turbine was emulated by controlling the torque of the DC motor. The WTE is used as a prime mover for Permanent Magnet Synchronous Machine (PMSM. In order to extract maximum power from the wind, PI and Fuzzy controllers were tested. Simulation results are given to show performance of proposed fuzzy control system in maximum power points tracking in a wind energy conversion system under various wind conditions. The strategy control was implemented in simulation using MATLAB/Simulink.

  6. Damping Force Tracking Control of MR Damper System Using a New Direct Adaptive Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Xuan Phu Do

    2015-01-01

    Full Text Available This paper presents a new direct adaptive fuzzy controller and its effectiveness is verified by investigating the damping force tracking control of magnetorheological (MR fluid based damper (MR damper in short system. In the formulation of the proposed controller, a model of interval type 2 fuzzy controller is combined with the direct adaptive control to achieve high performance in vibration control. In addition, H∞ (H infinity tracking technique is used in building a model of the direct adaptive fuzzy controller in which an enhanced iterative algorithm is combined with the fuzzy model. After establishing a closed-loop control structure to achieve high control performance, a cylindrical MR damper is adopted and damping force tracking results are obtained and discussed. In addition, in order to demonstrate the effectiveness of the proposed control strategy, two existing controllers are modified and tested for comparative work. It has been demonstrated from simulation and experiment that the proposed control scheme provides much better control performance in terms of damping force tracking error. This leads to excellent vibration control performance of the semiactive MR damper system associated with the proposed controller.

  7. Stabilization Controller Design for a class of Inverted Pendulums via Adaptive Fuzzy Sliding Mode Control

    Directory of Open Access Journals (Sweden)

    Heng Liu

    2013-07-01

    Full Text Available X–Z inverted pendulum is a new kind of inverted pendulum and it can move with the combination of the vertical and horizontal forces. This paper addresses  the control problem of X-Z inverted pendulum in the presents of system uncertainties and external disturbances, and an adaptive fuzzy sliding mode control approach is proposed. The fuzzy  system is used to approximate the system uncertainties and the complicated intermediate control functions in the backstepping control design. To update the parameters of the fuzzy system, a proper proportional-integral adaptation law is introduced.  Finally, simulation studies are done to show the stabilization of the X-Z inverted pendulum under the proposed method.

  8. Adaptive fuzzy control design for the molten steel level in a strip casting process

    Directory of Open Access Journals (Sweden)

    Y. J. Zhang

    2017-01-01

    Full Text Available This paper studies the adaptive fuzzy control problem of the molten steel level for a class of twin roll strip casting systems. Based on fuzzy logic systems (FLSs and the mean value theorem, a novel adaptive tracking controller with parameter updated laws is effectively designed. It is proved that all the closed-loop signals are uniformly bounded and the system tracking errors can asymptotically converge to zero by using the Lyapunov stability analysis. Simulation results of semi-experimental system dynamic model and parameters are provided to demonstrate the validity of the proposed adaptive fuzzy design approach.

  9. Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

    Institute of Scientific and Technical Information of China (English)

    LI Yan; FAN Xiao-ping

    2008-01-01

    An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network.The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level.The control level decides the signal tunings in an intersection with a fuzzy logic controller.The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one.Consequently the system performances are improved.A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections.So the AFC combined with the WCC can be applied in a road network for signal timings.Simulations of the AFC on a real traffic scenario have been conducted.Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

  10. Constant Power Control of a Proton Exchange Membrane Fuel Cell through Adaptive Fuzzy Sliding Mode

    Directory of Open Access Journals (Sweden)

    Minxiu Yan

    2013-05-01

    Full Text Available Fuel cell is a device that converts the chemical energy from a fuel into electricity through a chemical reaction with oxygen or another oxidizing agent. The paper describes a mathematical model of proton exchange membrane fuel cells by analyzing the working mechanism of the proton exchange membrane fuel cell. Furthermore, an adaptive fuzzy sliding mode controller is designed for the constant power output of PEMFC system. Simulation results prove that adaptive fuzzy sliding mode control has better control effect than conventional fuzzy sliding mode control.

  11. Position control of linear induction motor using an adaptive fuzzy integral: Back stepping controller

    Directory of Open Access Journals (Sweden)

    Bousserhane I.K.

    2006-01-01

    Full Text Available In this paper the position control of a linear induction motor using adaptive fuzzy back stepping design with integral action is proposed. First, the indirect field oriented control for LIM is derived. Then, an integral back stepping design for indirect field oriented control of LIM is proposed to compensate the uncertainties which occur in the control. Finally, the fuzzy integral-back stepping controller is investigated, where a simple fuzzy inference mechanism is used to achieve a position tracking objective under the mechanical parameters uncertainties. The effectiveness of the proposed control scheme is verified by numerical simulation. The numerical validation results of the proposed scheme have presented good performances compared to the conventional integral back stepping control.

  12. 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.

  13. Adaptive Control of MEMS Gyroscope Based on T-S Fuzzy Model

    Directory of Open Access Journals (Sweden)

    Yunmei Fang

    2015-01-01

    Full Text Available A multi-input multioutput (MIMO Takagi-Sugeno (T-S fuzzy model is built on the basis of a nonlinear model of MEMS gyroscope. A reference model is adjusted so that a local linear state feedback controller could be designed for each T-S fuzzy submodel based on a parallel distributed compensation (PDC method. A parameter estimation scheme for updating the parameters of the T-S fuzzy models is designed and analyzed based on the Lyapunov theory. A new adaptive law can be selected to be the former adaptive law plus a nonnegative in variable to guarantee that the derivative of the Lyapunov function is smaller than zero. The controller output is implemented on the nonlinear model and T-S fuzzy model, respectively, for the purpose of comparison. Numerical simulations are investigated to verify the effectiveness of the proposed control scheme and the correctness of the T-S fuzzy model.

  14. Adaptive fuzzy logic controller with direct action type structures for InnoSAT attitude control system

    Science.gov (United States)

    Bakri, F. A.; Mashor, M. Y.; Sharun, S. M.; Bibi Sarpinah, S. N.; Abu Bakar, Z.

    2016-10-01

    This study proposes an adaptive fuzzy controller for attitude control system (ACS) of Innovative Satellite (InnoSAT) based on direct action type structure. In order to study new methods used in satellite attitude control, this paper presents three structures of controllers: Fuzzy PI, Fuzzy PD and conventional Fuzzy PID. The objective of this work is to compare the time response and tracking performance among the three different structures of controllers. The parameters of controller were tuned on-line by adjustment mechanism, which was an approach similar to a PID error that could minimize errors between actual and model reference output. This paper also presents a Model References Adaptive Control (MRAC) as a control scheme to control time varying systems where the performance specifications were given in terms of the reference model. All the controllers were tested using InnoSAT system under some operating conditions such as disturbance, varying gain, measurement noise and time delay. In conclusion, among all considered DA-type structures, AFPID controller was observed as the best structure since it outperformed other controllers in most conditions.

  15. Closed Loop Speed Control of a BLDC Motor Drive Using Adaptive Fuzzy Tuned PI Controller

    Directory of Open Access Journals (Sweden)

    Sri Latha Eti

    2014-11-01

    Full Text Available Brushless DC Motors are widely used for many industrial applications because of their high efficiency, high torque and low volume. This paper proposed an improved Adaptive Fuzzy PI controller to control the speed of BLDC motor. This paper provides an overview of different tuning methods of PID Controller applied to control the speed of the transfer function model of the BLDC motor drive and then to the mathematical model of the BLDC motor drive. It is difficult to tune the parameters and get satisfied control characteristics by using normal conventional PI controller. The experimental results verify that Adaptive Fuzzy PI controller has better control performance than the conventional PI controller. The modeling, control and simulation of the BLDC motor have been done using the MATLAB/SIMULINK software. Also, the dynamic characteristics of the BLDC motor (i.e. speed and torque as well as currents and voltages of the inverter components are observed by using the developed model.

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

    Directory of Open Access Journals (Sweden)

    Tat-Bao-Thien Nguyen

    2014-01-01

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

  17. The Self-Adaptive Fuzzy PID Controller in Actuator Simulated Loading System

    Directory of Open Access Journals (Sweden)

    Chuanhui Zhang

    2013-05-01

    Full Text Available This paper analyzes the structure principle of the actuator simulated loading system with variable stiffness, and establishes the simplified model. What’s more, it also does a research on the application of the self-adaptive tuning of fuzzy PID(Proportion Integration Differentiation in actuator simulated loading system with variable stiffness. Because the loading system is connected with the steering system by a spring rod, there must be strong coupling. Besides, there are also the parametric variations accompanying with the variations of the stiffness. Based on compensation from the feed-forward control on the disturbance brought by the motion of steering engine, the system performance can be improved by using fuzzy adaptive adjusting PID control to make up the changes of system parameter caused by the changes of the stiffness. By combining the fuzzy control with traditional PID control, fuzzy adaptive PID control is able to choose the parameters more properly.

  18. Adaptive Fuzzy Sliding Mode Control of MEMS Gyroscope with Finite Time Convergence

    Directory of Open Access Journals (Sweden)

    Jianxin Ren

    2016-01-01

    Full Text Available This paper presents adaptive fuzzy finite time sliding mode control of microelectromechanical system gyroscope with uncertainty and external disturbance. Firstly, fuzzy system is employed to approximate the uncertainty nonlinear dynamics. Secondly, nonlinear sliding mode hypersurface and double exponential reaching law are selected to design the finite time convergent sliding mode controller. Thirdly, based on Lyapunov methods, adaptive laws are presented to adjust the fuzzy weights and the system can be guaranteed to be stable. Finally, the effectiveness of the proposed method is verified with simulation.

  19. Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.

  20. Adaptive Fuzzy Control for Uncertain Fractional-Order Financial Chaotic Systems Subjected to Input Saturation

    Science.gov (United States)

    Wang, Chenhui

    2016-01-01

    In this paper, control of uncertain fractional-order financial chaotic system with input saturation and external disturbance is investigated. The unknown part of the input saturation as well as the system’s unknown nonlinear function is approximated by a fuzzy logic system. To handle the fuzzy approximation error and the estimation error of the unknown upper bound of the external disturbance, fractional-order adaptation laws are constructed. Based on fractional Lyapunov stability theorem, an adaptive fuzzy controller is designed, and the asymptotical stability can be guaranteed. Finally, simulation studies are given to indicate the effectiveness of the proposed method. PMID:27783648

  1. 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.

  2. Observed-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems With Dead-Zone.

    Science.gov (United States)

    Tong, Shaocheng; Sui, Shuai; Li, Yongming

    2015-12-01

    In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.

  3. Adaptive fuzzy sliding mode control for synchronization of uncertain fractional order chaotic systems

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Tsung-Chih, E-mail: tclin@fcu.edu.tw [Department of Electronic Engineering, Feng-Chia University, Taichung, Taiwan (China); Lee, Tun-Yuan [Department of Electronic Engineering, Feng-Chia University, Taichung, Taiwan (China); Balas, Valentina E. [Aurel Vlaicu University of Arad, B-dul Revolutiei 77, 310130 Arad (Romania)

    2011-10-15

    Highlights: > We study uncertain fractional order chaotic systems synchronization. > Lyapunov synthesis is used to derive control law and adaptive laws. > Based on sliding mode control, chattering phenomena in the control effort can be reduced. - Abstract: This paper deals with chaos synchronization between two different uncertain fractional order chaotic systems based on adaptive fuzzy sliding mode control (AFSMC). With the definition of fractional derivatives and integrals, a fuzzy Lyapunov synthesis approach is proposed to tune free parameters of the adaptive fuzzy controller on line by output feedback control law and adaptive law. Moreover, chattering phenomena in the control efforts can be reduced. The sliding mode design procedure not only guarantees the stability and robustness of the proposed AFSMC, but also the external disturbance on the synchronization error can be attenuated. The simulation example is included to confirm validity and synchronization performance of the advocated design methodology.

  4. Adaptive fuzzy switched control design for uncertain nonholonomic systems with input nonsmooth constraint

    Science.gov (United States)

    Li, Yongming; Tong, Shaocheng

    2016-10-01

    In this paper, a fuzzy adaptive switched control approach is proposed for a class of uncertain nonholonomic chained systems with input nonsmooth constraint. In the control design, an auxiliary dynamic system is designed to address the input nonsmooth constraint, and an adaptive switched control strategy is constructed to overcome the uncontrollability problem associated with x0(t0) = 0. By using fuzzy logic systems to tackle unknown nonlinear functions, a fuzzy adaptive control approach is explored based on the adaptive backstepping technique. By constructing the combination approximation technique and using Young's inequality scaling technique, the number of the online learning parameters is reduced to n and the 'explosion of complexity' problem is avoid. It is proved that the proposed method can guarantee that all variables of the closed-loop system converge to a small neighbourhood of zero. Two simulation examples are provided to illustrate the effectiveness of the proposed control approach.

  5. Applied research on adaptive fuzzy-PI double model control for hot-box

    Institute of Scientific and Technical Information of China (English)

    WU Yi-feng; ZHANG Jian-li; BAI Tian

    2006-01-01

    Hot-box is a device used widely in the world for studying thermodynamic properties of architecture material and types of walls. It can run both static and dynamic experiments, and its demand for controlling is high. Because it adopts traditional PI control presently, and is mainly used for static experiments, its dynamic response is bad. Therefore, this paper applies adaptive fuzzy control, which follows dynamic movement quite well to the hotbox device. At the same time, considering the characteristic that the stable state quality is high within little error of traditional PI control, it combines the adaptive fuzzy control with quantity factor and proportion factor self-adjusting online and PI control to be a new double mode control using different control models at different conditions. The results of hotbox controlling experiments indicate that this control system is better than PI control or single fuzzy control both at response and precision.

  6. Sliding mode control of wind-induced vibrations using fuzzy sliding surface and gain adaptation

    Science.gov (United States)

    Thenozhi, Suresh; Yu, Wen

    2016-04-01

    Although fuzzy/adaptive sliding mode control can reduce the chattering problem in structural vibration control applications, they require the equivalent control and the upper bounds of the system uncertainties. In this paper, we used fuzzy logic to approximate the standard sliding surface and designed a dead-zone adaptive law for tuning the switching gain of the sliding mode control. The stability of the proposed controller is established using Lyapunov stability theory. A six-storey building prototype equipped with an active mass damper has been used to demonstrate the effectiveness of the proposed controller towards the wind-induced vibrations.

  7. Method study on fuzzy-PID adaptive control of electric-hydraulic hitch system

    Science.gov (United States)

    Li, Mingsheng; Wang, Liubu; Liu, Jian; Ye, Jin

    2017-03-01

    In this paper, fuzzy-PID adaptive control method is applied to the control of tractor electric-hydraulic hitch system. According to the characteristics of the system, a fuzzy-PID adaptive controller is designed and the electric-hydraulic hitch system model is established. Traction control and position control performance simulation are carried out with the common PID control method. A field test rig was set up to test the electric-hydraulic hitch system. The test results showed that, after the fuzzy-PID adaptive control is adopted, when the tillage depth steps from 0.1m to 0.3m, the system transition process time is 4s, without overshoot, and when the tractive force steps from 3000N to 7000N, the system transition process time is 5s, the system overshoot is 25%.

  8. Adaptive Fuzzy Control for Nonlinear Fractional-Order Uncertain Systems with Unknown Uncertainties and External Disturbance

    OpenAIRE

    2015-01-01

    In this paper, the problem of robust control of nonlinear fractional-order systems in the presence of uncertainties and external disturbance is investigated. Fuzzy logic systems are used for estimating the unknown nonlinear functions. Based on the fractional Lyapunov direct method and some proposed Lemmas, an adaptive fuzzy controller is designed. The proposed method can guarantee all the signals in the closed-loop systems remain bounded and the tracking errors converge to an arbitrary small ...

  9. Fuzzy Backstepping Torque Control Of Passive Torque Simulator With Algebraic Parameters Adaptation

    Science.gov (United States)

    Ullah, Nasim; Wang, Shaoping; Wang, Xingjian

    2015-07-01

    This work presents fuzzy backstepping control techniques applied to the load simulator for good tracking performance in presence of extra torque, and nonlinear friction effects. Assuming that the parameters of the system are uncertain and bounded, Algebraic parameters adaptation algorithm is used to adopt the unknown parameters. The effect of transient fuzzy estimation error on parameters adaptation algorithm is analyzed and the fuzzy estimation error is further compensated using saturation function based adaptive control law working in parallel with the actual system to improve the transient performance of closed loop system. The saturation function based adaptive control term is large in the transient time and settles to an optimal lower value in the steady state for which the closed loop system remains stable. The simulation results verify the validity of the proposed control method applied to the complex aerodynamics passive load simulator.

  10. Adaptive fuzzy decentralised control for stochastic nonlinear large-scale systems in pure-feedback form

    Science.gov (United States)

    Tong, Shaocheng; Xu, Yinyin; Li, Yongming

    2015-06-01

    This paper is concerned with the problem of adaptive fuzzy decentralised output-feedback control for a class of uncertain stochastic nonlinear pure-feedback large-scale systems with completely unknown functions, the mismatched interconnections and without requiring the states being available for controller design. With the help of fuzzy logic systems approximating the unknown nonlinear functions, a fuzzy state observer is designed estimating the unmeasured states. Therefore, the nonlinear filtered signals are incorporated into the backstepping recursive design, and an adaptive fuzzy decentralised output-feedback control scheme is developed. It is proved that the filter system converges to a small neighbourhood of the origin based on appropriate choice of the design parameters. Simulation studies are included illustrating the effectiveness of the proposed approach.

  11. Combined indirect and direct method for adaptive fuzzy output feedback control of nonlinear system

    Institute of Scientific and Technical Information of China (English)

    Ding Quanxin; Chen Haitong; Jiang Changsheng; Chen Zongji

    2007-01-01

    A novel control method for a general class of nonlinear systems using fuzzy logic systems (FLSs) is presertted.Indirect and direct methods are combined to design the adaptive fuzzy output feedback controller and a high-gain observer is used to estimate the derivatives of the system output. The closed-loop system is proven to be semiglobally uniformly ultimately bounded. In addition, it is shown that if the approximation accuracy of the fuzzy logic system is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussion.

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

    Directory of Open Access Journals (Sweden)

    Xingjian Wang

    2013-01-01

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

  13. Adaptive Fuzzy Sliding Mode Tracking Control of Uncertain Underactuated Nonlinear Systems: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Faten Baklouti

    2016-01-01

    Full Text Available The trajectory tracking of underactuated nonlinear system with two degrees of freedom is tackled by an adaptive fuzzy hierarchical sliding mode controller. The proposed control law solves the problem of coupling using a hierarchical structure of the sliding surfaces and chattering by adopting different reaching laws. The unknown system functions are approximated by fuzzy logic systems and free parameters can be updated online by adaptive laws based on Lyapunov theory. Two comparative studies are made in this paper. The first comparison is between three different expressions of reaching laws to compare their abilities to reduce the chattering phenomenon. The second comparison is made between the proposed adaptive fuzzy hierarchical sliding mode controller and two other control laws which keep the coupling in the underactuated system. The tracking performances of each control law are evaluated. Simulation examples including different amplitudes of external disturbances are made.

  14. Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay.

    Science.gov (United States)

    Khazaee, Mostafa; Markazi, Amir H D; Omidi, Ehsan

    2015-11-01

    In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound.

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

    Directory of Open Access Journals (Sweden)

    Lili Zhang

    2014-01-01

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

  16. Adaptive fuzzy neural network control design via a T-S fuzzy model for a robot manipulator including actuator dynamics.

    Science.gov (United States)

    Wai, Rong-Jong; Yang, Zhi-Wei

    2008-10-01

    This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems.

  17. Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

    Indian Academy of Sciences (India)

    Abdurahman Kadir; Xing-Yuan Wang; Yu-Zhang Zhao

    2011-06-01

    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 online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.

  18. Adaptive Fuzzy Containment Control for Uncertain Nonlinear Multiagent Systems

    Directory of Open Access Journals (Sweden)

    Yang Yu

    2014-01-01

    Full Text Available This paper considers the containment control problem for uncertain nonlinear multiagent systems under directed graphs. The followers are governed by nonlinear systems with unknown dynamics while the multiple leaders are neighbors of a subset of the followers. Fuzzy logic systems (FLSs are used to identify the unknown dynamics and a distributed state feedback containment control protocol is proposed. This result is extended to the output feedback case, where observers are designed to estimate the unmeasurable states. Then, an output feedback containment control scheme is presented. The developed state feedback and output feedback containment controllers guarantee that the states of all followers converge to the convex hull spanned by the dynamic leaders. Based on Lyapunov stability theory, it is proved that the containment control errors are uniformly ultimately bounded (UUB. An example is provided to show the effectiveness of the proposed control method.

  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. Adaptive Fuzzy Tracking Control for a Class of MIMO Nonlinear Systems in Nonstrict-Feedback Form.

    Science.gov (United States)

    Chen, Bing; Lin, Chong; Liu, Xiaoping; Liu, Kefu

    2015-12-01

    This paper focuses on the problem of fuzzy adaptive control for a class of multiinput and multioutput (MIMO) nonlinear systems in nonstrict-feedback form, which contains the strict-feedback form as a special case. By the condition of variable partition, a new fuzzy adaptive backstepping is proposed for such a class of nonlinear MIMO systems. The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin. The main advantage of this paper is that a control approach is systematically derived for nonlinear systems with strong interconnected terms which are the functions of all states of the whole system. Simulation results further illustrate the effectiveness of the suggested approach.

  1. Fuzzy adaptive tracking control within the full envelope for an unmanned aerial vehicle

    Institute of Scientific and Technical Information of China (English)

    Liu Zhi; Wang Yong

    2014-01-01

    Motivated by the autopilot of an unmanned aerial vehicle (UAV) with a wide flight enve-lope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller (FATC) is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality (LMI), and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded (UUB) predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.

  2. Fuzzy adaptive tracking control within the full envelope for an unmanned aerial vehicle

    Directory of Open Access Journals (Sweden)

    Liu Zhi

    2014-10-01

    Full Text Available Motivated by the autopilot of an unmanned aerial vehicle (UAV with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller (FATC is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality (LMI, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded (UUB predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.

  3. Foundations Of Fuzzy Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    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......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...

  4. Foundations Of Fuzzy Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    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......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...... 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...

  5. Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

    Science.gov (United States)

    Tong, Shaocheng; Li, Yongming

    2017-02-01

    This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

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

    Science.gov (United States)

    Chen, Zhigang; Qu, Jiangang

    2017-09-01

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

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

    Science.gov (United States)

    Jin, Liang-Ping; Li, Hong-Chan

    2013-03-01

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

  8. Simulation and analysis of a Truck Model's ride comfort based on fuzzy adaptive control theory

    Institute of Scientific and Technical Information of China (English)

    JIANG Li-biao; WANG Deng-feng; NI Qiang; TAN Wei-ming

    2007-01-01

    This paper tried to analyse and verify the fuzzy adaptive control strategy of electronic control air suspension system for heavy truck. Created the seven-freedoms vehicle suspension model, and the road input model; with Matlab/Simulink toolboxes and modules, built dynamical system simulation model for heavy truck with air suspension, fuzzy adaptive control model, height control model for air spring, and intelligent control and analyse on root mean square value of acceleration of gravity center of the vehicle under excitation of road. Results show that the fuzzy control had less help to the body vibration on the better pavement, but had the better benefit on the bad road, and the vehicle's root mean square value of acceleration of gravity center is less than passive suspension's obviously.

  9. Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults

    Science.gov (United States)

    Hou, Yingxue; Tong, Shaocheng; Li, Yongming

    2016-11-01

    This paper investigates the problem of fault-tolerant control (FTC) for a class of switched nonlinear systems. These systems are under arbitrary switchings and are subject to both lock-in-place and loss-of-effectiveness actuator faults. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. Under the framework of the backstepping control design, FTC, fuzzy adaptive control and common Lyapunov function stability theory, an adaptive fuzzy control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop switched system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighbourhood of the origin. Two simulation examples are provided to illustrate the effectiveness of the proposed approach.

  10. Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory

    Science.gov (United States)

    Rigatos, Gerasimos G.

    2013-10-01

    An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.

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

    CERN Document Server

    Xue Yue Ju

    2003-01-01

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

  12. Study of Multimedia Streams Dynamic Rate Control Based on Fuzzy Adaptive PID

    Institute of Scientific and Technical Information of China (English)

    SUN Yan-fei; ZHANG Shun-yi; SHI Jin; WANG Jiang-tao

    2005-01-01

    A Multimedia streams dynamic rate control algorithm based on Fuzzy adaptive PID (MFPID) has been proposed to implement multimedia streams' end sending rate on-line self-regulating and smoothing, and to track system resources in time, so that it can avoid system's regulating oscillation and guarantee system's stability. And, some work has been done to analyze adaptive session model of multimedia streams, to implement future available bandwidth estimation of IP network, to achieve PID parameters' on-line self-tuning by fuzzy controlling. Simulation validated the theoretical results of MFPID.

  13. Robust Adaptive Fuzzy Output Tracking Control for a Class of Twin-Roll Strip Casting Systems

    Directory of Open Access Journals (Sweden)

    Yu-Jun Zhang

    2017-01-01

    Full Text Available This paper is concerned with the adaptive fuzzy control problem for a class of twin-roll strip casting systems. By using fuzzy logic systems (FLSs to approximate the compounded nonlinear functions, a novel robust output tracking controller with adaptation laws is designed based on the high gain observer. First, the nonlinear dynamic equations for the roll gap and the molten steel level are constructed, respectively. Then, the mean value theorem is employed to transform the nonaffine nonlinear systems to the corresponding affine nonlinear systems. Moreover, it is also proved that all the closed-loop signals are bounded and the systems output tracking errors can converge to the desired neighborhoods of the origin via the Lyapunov stability analysis. Finally, simulation results, based on semiexperimental system dynamic model and parameters, are worked out to show the effectiveness of the proposed adaptive fuzzy design method.

  14. Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator

    Directory of Open Access Journals (Sweden)

    Srinivasan Alavandar

    2008-01-01

    Full Text Available The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. 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. This paper presents the control of six degrees of freedom robot arm (PUMA Robot using Adaptive Neuro Fuzzy Inference System (ANFIS based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers

  15. 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.

  16. FPGA-based adaptive backstepping fuzzy control for a micro-positioning Scott-Russell mechanism

    Science.gov (United States)

    Fung, Rong-Fong; Weng, Ming-Hong; Kung, Ying-Shieh

    2009-11-01

    This paper utilizes the field programmable gate array (FPGA) and Nios II embedded processor technologies to design a controller IC for a micro-positioning Scott-Russell (SR) mechanism, which is driven by a piezoelectric actuator (PA) and its hysteresis phenomenon is described by Bouc-Wen hysteresis model. For the controller design, the adaptive backstepping fuzzy control (ABFC) method is developed to compensate the PA's hysteresis and achieve the motion tracking control. The fuzzy logic method (FLM) is utilized to find the best adaptation gain of the adaptation law and control gain of the stabilization controls. This ABFC controller method can improve the transient and asymptotic tracking performances, and make the SR mechanism keep good working performance when external disturbances is added in the control system. Finally, we successfully apply the system-on-a-programmable-chip (SoPC) technologies to develop the motion controller IC, and achieve the advantages of reduced space, high performance and low cost.

  17. Research of robust adaptive trajectory linearization control based on T-S fuzzy system

    Institute of Scientific and Technical Information of China (English)

    Jiang Changsheng; Zhang Chunyu; Zhu Liang

    2008-01-01

    A robust adaptive trajectory linearization control (RATLC) algorithm for a class of nonlinear systems with uncertainty and disturbance based on the T-S fuzzy system is presented. The unknown disturbance and uncertainty are estimated by the T-S fuzzy system, and a robust adaptive control law is designed by the Lyapunov theory. Irrespective of whether the dimensions of the system and the rules of the fuzzy system are large or small, there is only one parameter adjusting on line. Uniformly ultimately boundedness of all signals of the composite closed-loop system are proved by theory analysis. Finally, a numerical example is studied based on the proposed method. The simulation results demonstrate the effectiveness and robustness of the control scheme.

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

    CERN Document Server

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-06-01

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

  20. The Model Reference Adaptive Fuzzy Control for the Vehicle Semi-Active Suspension

    Institute of Scientific and Technical Information of China (English)

    管继富; 侯朝桢; 顾亮; 武云鹏

    2003-01-01

    The LQG control system is employed as vehicle suspension's optimal target system, which has an adaptive ability to the road conditions and vehicle speed in a limited bandwidth. In order to keep the optimal performances when the suspension parameters change, a model reference adaptive fuzzy control (MRAFC) strategy is presented. The LQG control system serves as the reference model in the MRAFC system. The simulation results indicate that the presented MRAFC system can adapt to the parameters variation of vehicle suspension and track the optimality of the LQG control system, the presented vehicle suspension MRAFC system has the ability to adapt to road conditions and suspension parameters change.

  1. Adaptive backstepping sliding mode control with fuzzy monitoring strategy for a kind of mechanical system.

    Science.gov (United States)

    Song, Zhankui; Sun, Kaibiao

    2014-01-01

    A novel adaptive backstepping sliding mode control (ABSMC) law with fuzzy monitoring strategy is proposed for the tracking-control of a kind of nonlinear mechanical system. The proposed ABSMC scheme combining the sliding mode control and backstepping technique ensure that the occurrence of the sliding motion in finite-time and the trajectory of tracking-error converge to equilibrium point. To obtain a better perturbation rejection property, an adaptive control law is employed to compensate the lumped perturbation. Furthermore, we introduce fuzzy monitoring strategy to improve adaptive capacity and soften the control signal. The convergence and stability of the proposed control scheme are proved by using Lyaponov's method. Finally, numerical simulations demonstrate the effectiveness of the proposed control scheme.

  2. Some Improvements on Active Queue Management Mechanism Based on Adaptive Fuzzy Control

    Directory of Open Access Journals (Sweden)

    Nguyen Kim Quoc

    2015-11-01

    Full Text Available Active queue management operates at network nodes to control the number of packets in the queue of nodes, by actively receiving packets when the queue is not full, removing packets when the queue is full or notifying bottlenecks even in the embryonic period of the bottlenecks due to to-be-full queue. In recent years, scientists have used fuzzy logic to improve queue management mechanisms. Overall, these improvements have used Mamdani fuzzy system with a fixed structure with triangular functions for input and output variables, so they do not adapt to the changing state of the network. We propose a adaptive fuzzy control (AFC model to improve the effectiveness of active queue management mechanisms.

  3. Robust adaptive fuzzy control for a class of perturbed pure-feedback nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    Jianjiang YU; Tianping ZHANG; Haijun GU

    2004-01-01

    A new design scheme of direct adaptive fuzzy controller for a class of perturbed pure-feedback nonlinear systems is proposed. The design is based on backstepping and the approximation capability of the first type fuzzy systems. A continuous robust term is adopted to minif-y the influence of modeling errors or disturbances. By introducing the modified integral-type Lyapunov function, the approach is able to avoid the requirement of the upper bound of the first time derivation of the high frequency control gain. Through theoretical analysis, the closed-loop control system is proven to be semi-globally uniformly ultimately bounded, with tracking error converging to a residual set.

  4. Sequential Adaptive Fuzzy Inference System Based Intelligent Control of Robot Manipulators

    Directory of Open Access Journals (Sweden)

    Sahraoui Mustapha

    2014-10-01

    Full Text Available The present paper is dedicated to the presentation and implementation of an optimized technique allowing an on-line estimation of a robot manipulator parameters to use them in a computed torque control. Indeed the proposed control law needs the exact robot model to give good performances. The complexity of the robot manipulator and its strong non-linearity makes it hard to know its parameters. Therefore, we propose in this paper to use neuro-fuzzy networks Sequential Adaptive Fuzzy Inference System (SAFIS to estimate the parameters of the controlled robot manipulator.

  5. A Novel Fuzzy Logic Based Adaptive Super-Twisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    OpenAIRE

    Abdul Kareem; Mohammad Fazle Azeem

    2012-01-01

    This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness ...

  6. Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller.

    Science.gov (United States)

    Lin, Chih-Min; Yang, Ming-Shu; Chao, Fei; Hu, Xiao-Min; Zhang, Jun

    2016-10-01

    This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations.

  7. Robust chaos synchronization based on adaptive fuzzy delayed feedback $\\mathcal{H}_{∞}$ control

    Indian Academy of Sciences (India)

    Choon Ki Ahn

    2012-03-01

    In this paper, we propose a new adaptive $\\mathcal_{∞}$ synchronization strategy, called an adaptive fuzzy delayed feedback $\\mathcal_{∞}$ synchronization (AFDFHS) strategy, for chaotic systems with uncertain parameters and external disturbances. Based on Lyapunov–Krasovskii theory, Takagi–Sugeno (T–S) fuzzy model and adaptive delayed feedback $\\mathcal_{∞}$ control scheme, the AFDFHS controller is presented such that the synchronization error system is asymptotically stable with a guaranteed $\\mathcal_{∞}$ performance. It is shown that the design of the AFDFHS controller with adaptive law can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed AFDFHS approach.

  8. Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.

    Science.gov (United States)

    Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios

    2013-10-01

    In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not

  9. Adaptive-Fuzzy Controller Based Shunt Active Filter for Power Line Conditioners

    Directory of Open Access Journals (Sweden)

    KamalaKanta Mahapatra

    2011-08-01

    Full Text Available This paper presents a novel Fuzzy Logic Controller (FLC in conjunction with Phase Locked Loop (PLL based shunt active filter for Power Line Conditioners (PLCs to improve the power quality in the distribution system. The active filter is implemented with current controlled Voltage Source Inverter (VSI for compensating current harmonics and reactive power at the point of common coupling. The VSI gate control switching pulses are derived from proposed Adaptive-Fuzzy-Hysteresis Current Controller (HCC and this method calculates the hysteresis bandwidth effectively using fuzzy logic. The bandwidth can be adjusted based on compensation current variation, which is used to optimize the required switching frequency and improves active filter substantially. These shunt active power filter system is investigated and verified under steady and transient-state with non-linear load conditions. This shunt active filter is in compliance with IEEE 519 and IEC 61000-3 recommended harmonic standards.

  10. Output feedback adaptive fuzzy control of uncertain MIMO nonlinear systems with unknown input nonlinearities.

    Science.gov (United States)

    Shahnazi, Reza

    2015-01-01

    An adaptive fuzzy output feedback controller is proposed for a class of uncertain MIMO nonlinear systems with unknown input nonlinearities. The input nonlinearities can be backlash-like hysteresis or dead-zone. Besides, the gains of unknown input nonlinearities are unknown nonlinear functions. Based on universal approximation theorem, the unknown nonlinear functions are approximated by fuzzy systems. The proposed method does not need the availability of the states and an observer based on strictly positive real (SPR) theory is designed to estimate the states. An adaptive robust structure is used to cope with fuzzy approximation error and external disturbances. The semi-global asymptotic stability of the closed-loop system is guaranteed via Lyapunov approach. The applicability of the proposed method is also shown via simulations.

  11. Decentralized adaptive fuzzy control of time-delayed interconnected systems with unknown backlash-like hysteresis

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The problem of decentralized adaptive fuzzy control for a class of time-delayed interconnected nonlinear systems with unknown backlash-like hystersis is discussed. On the basis of the principle of variable structure control (VSC) and by using the fuzzy systems with linear adjustable parameters that are used to approximate plant unknown functions, a novel decentralized adaptive fuzzy control strategy with a supervisory controller is developed. A general method, which is modeled the backlash-like hysteresis, is proposed and removes the assumption that the boundedness of disturbance, and the slope of the backlash-like hystersis are known constants. Furthermore, the interconnection term is supposed to be pth-order polynomial in time-delayed states. In addition, the plant dynamic uncertainty and modeling errors are adaptively compensated by adjusting the parameters and gains on-line for each subsystems. By theoretical analysis, it is shown that the closed-loop fuzzy control systems are globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.

  12. Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms.

    Science.gov (United States)

    Liu, B D; Chen, C Y; Tsao, J Y

    2001-01-01

    In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.

  13. Adaptive fuzzy sliding mode control for gantry crane as varying rope length

    Directory of Open Access Journals (Sweden)

    TRINH LUONG MIEN

    2016-08-01

    Full Text Available Gantry crane is used quite commonly in hazardous areas, which increasingly requires strict conrol of the gantry crane operation process to improve efficiency and ensure safe gantry crane opeartion. Automated the gantry crane operating process is being applied pupular currently. Gantry crane is often affected by large noise, having the varying- model parameters, so that proposed a apdaptive fuzzy combining sliding mode controller for the gantry crane in this article. This control method derived from combining the sliding surfaces of three subsystem of the gantry crane (trolley position, rope length, anti-swing to draw out two system sliding surfaces: the trolley positon with the anti-swing and the rope length and the anti-swing. On the based of the sliding mode control principle,drawn out the equivalent controller and the switching controller for gantry crane. But due to the uncertain parameters - nonlinear model of gantry crane with the bound disturbances, combining the fuzzy approximate method, defined the fuzzy controller (used to minic the equivalent controller and the compensation controller for the difference between the equivalent controller and the fuzzy controller (used as the switching controller for two system control inputs: trolley position and rope length The adaptive control laws for these controllers were deduced from Lyapunov’s stable criteria to asymptotically stabilize the sliding surfaces. Simulation results demonstrated the feasibility of the suggested method through grantry crane in the hazard areas.

  14. Chattering free adaptive fuzzy terminal sliding mode control for second order nonlinear system

    Institute of Scientific and Technical Information of China (English)

    Jinkun LIU; Fuchun SUN

    2006-01-01

    A novel fuzzy terminal sliding mode control (FTSMC) scheme is proposed for position tracking of a class of second-order nonlinear uncertain system. In the proposed scheme, we integrate input-output linearization technique to cancel the nonlinearities. By using a function-augmented sliding hyperplane, it is guaranteed that the output tracking error converges to zero in finite time which can be set arbitrarily. The proposed scheme eliminates reaching phase problem, so that the closed-loop system always shows invariance property to parameter uncertainties. Fuzzy logic systems are used to approximate the unknown system functions and switch item. Robust adaptive law is proposed to reduce approximation errors between true nonlinear functions and fuzzy systems, thus chattering phenomenon can be eliminated. Stability of the proposed control scheme is proved and the scheme is applied to an inverted pendulum system. Simulation studies are provided to confirm performance and effectiveness of the proposed control approach.

  15. Decentralized model reference adaptive sliding mode control based on fuzzy model

    Institute of Scientific and Technical Information of China (English)

    Gu Haijun; Zhang Tianping; Shen Qikun

    2006-01-01

    A new design scheme of decentralized model reference adaptive sliding mode controller for a class of MIMO nonlinear systems with the high-order interconnections is proposed. The design is based on the universal approximation capability of the Takagi - Seguno (T-S) fuzzy systems. Motivated by the principle of certainty equivalentcontrol, a decentralized adaptive controller is designed to achieve the tracking objective without computation of the T-S fuzz ymodel. The approach does not require the upper bound of the uncertainty term to be known through some adaptive estimation. By theoretical analysis, the closed-loop fuzzy control system is proven to be globally stable in the sense that all signalsinvolved are bounded, with tracking errors converging to zero. Simulation results demonstrate the effectiveness of the approach.

  16. Station-keeping control for a stratospheric airship platform via fuzzy adaptive backstepping approach

    Science.gov (United States)

    Yang, Yueneng; Wu, Jie; Zheng, Wei

    2013-04-01

    This paper presents a novel approach for station-keeping control of a stratospheric airship platform in the presence of parametric uncertainty and external disturbance. First, conceptual design of the stratospheric airship platform is introduced, including the target mission, configuration, energy sources, propeller and payload. Second, the dynamics model of the airship platform is presented, and the mathematical model of its horizontal motion is derived. Third, a fuzzy adaptive backstepping control approach is proposed to develop the station-keeping control system for the simplified horizontal motion. The backstepping controller is designed assuming that the airship model is accurately known, and a fuzzy adaptive algorithm is used to approximate the uncertainty of the airship model. The stability of the closed-loop control system is proven via the Lyapunov theorem. Finally, simulation results illustrate the effectiveness and robustness of the proposed control approach.

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

    Directory of Open Access Journals (Sweden)

    Saifullah Khalid

    2016-09-01

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

  18. Adaptive Fuzzy Control System of Servomechanism for Electro-Discharge Machining Combined with Ultrasonic Vibration

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM co...

  19. Controlling fractional order chaotic systems based on Takagi-Sugeno fuzzy model and adaptive adjustment mechanism

    Energy Technology Data Exchange (ETDEWEB)

    Zheng Yongai, E-mail: zhengyongai@163.co [Department of Computer, Yangzhou University, Yangzhou, 225009 (China); Nian Yibei [School of Energy and Power Engineering, Yangzhou University, Yangzhou, 225009 (China); Wang Dejin [Department of Computer, Yangzhou University, Yangzhou, 225009 (China)

    2010-12-01

    In this Letter, a kind of novel model, called the generalized Takagi-Sugeno (T-S) fuzzy model, is first developed by extending the conventional T-S fuzzy model. Then, a simple but efficient method to control fractional order chaotic systems is proposed using the generalized T-S fuzzy model and adaptive adjustment mechanism (AAM). Sufficient conditions are derived to guarantee chaos control from the stability criterion of linear fractional order systems. The proposed approach offers a systematic design procedure for stabilizing a large class of fractional order chaotic systems from the literature about chaos research. The effectiveness of the approach is tested on fractional order Roessler system and fractional order Lorenz system.

  20. An adaptive fuzzy-sliding lateral control strategy of automated vehicles based on vision navigation

    Science.gov (United States)

    Guo, Jinghua; Li, Linhui; Li, Keqiang; Wang, Rongben

    2013-10-01

    Lateral control is considered to be one of the toughest challenges in the development of automated vehicles due to their features of nonlinearities, parametric uncertainties and external disturbances. In order to overcome these difficulties, an adaptive fuzzy-sliding mode control strategy used for lateral control of vision-based automated vehicles is proposed in this paper. First, a vision algorithm is designed to provide accurate location information of vehicle relative to reference path. Then, an adaptive fuzzy-sliding mode lateral controller is proposed to counteract parametric uncertainties and strong nonlinearities, and the asymptotic stability of the closed-loop lateral control system is proven by the Lyapunov theory. Finally, experimental results indicate that the proposed algorithm can achieve favourable tracking performance, and it has strong robustness.

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

    Directory of Open Access Journals (Sweden)

    E.A. Ramadan

    2014-09-01

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

  2. Adaptive Fuzzy Control of Strict-Feedback Nonlinear Time-Delay Systems With Unmodeled Dynamics.

    Science.gov (United States)

    Yin, Shen; Shi, Peng; Yang, Hongyan

    2016-08-01

    In this paper, an approximated-based adaptive fuzzy control approach with only one adaptive parameter is presented for a class of single input single output strict-feedback nonlinear systems in order to deal with phenomena like nonlinear uncertainties, unmodeled dynamics, dynamic disturbances, and unknown time delays. Lyapunov-Krasovskii function approach is employed to compensate the unknown time delays in the design procedure. By combining the advances of the hyperbolic tangent function with adaptive fuzzy backstepping technique, the proposed controller guarantees the semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system from the mean square point of view. Two simulation examples are finally provided to show the superior effectiveness of the proposed scheme.

  3. Composite Adaptive Fuzzy Output Feedback Control Design for Uncertain Nonlinear Strict-Feedback Systems With Input Saturation.

    Science.gov (United States)

    Li, Yongming; Tong, Shaocheng; Li, Tieshan

    2015-10-01

    In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial-parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach.

  4. Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)

    Science.gov (United States)

    Wade, Robert L.; Walker, Gregory W.

    1996-05-01

    The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.

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

    KAUST Repository

    Elmetennani, Shahrazed

    2014-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Doaa M. Atia

    2017-05-01

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

  7. Adaptive fuzzy integral sliding mode velocity control for the cutting system of a trench cutter

    Institute of Scientific and Technical Information of China (English)

    Qi-yan TIAN; Jian-hua WEI; Jin-hui FANG‡; Kai GUO

    2016-01-01

    This paper presents a velocity controller for the cutting system of a trench cutter (TC). The cutting velocity of a cutting system is affected by the unknown load characteristics of rock and soil. In addition, geological conditions vary with time. Due to the complex load characteristics of rock and soil, the cutting load torque of a cutter is related to the geological conditions and the feeding velocity of the cutter. Moreover, a cutter’s dynamic model is subjected to uncertainties with unknown effects on its function. In this study, to deal with the particular characteristics of a cutting system, a novel adaptive fuzzy integral sliding mode control (AFISMC) is designed for controlling cutting velocity. The model combines the robust characteristics of an integral sliding mode controller with the adaptive adjusting characteristics of an adaptive fuzzy controller. The AFISMC cutting velocity con-troller is synthesized using the backstepping technique. The stability of the whole system including the fuzzy inference system, integral sliding mode controller, and the cutting system is proven using the Lyapunov theory. Experiments have been conducted on a TC test bench with the AFISMC under different operating conditions. The experimental results demonstrate that the proposed AFISMC cutting velocity controller gives a superior and robust velocity tracking performance.

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

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

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

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

    Science.gov (United States)

    Wu, Zhengping; Wu, Hao

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

  10. Adaptive control of parallel manipulators via fuzzy-neural network algorithm

    Institute of Scientific and Technical Information of China (English)

    Dachang ZHU; Yuefa FANG

    2007-01-01

    This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms (FNNA). With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme,we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom (DoF). The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.

  11. Computationally Efficient Adaptive Type-2 Fuzzy Control of Flexible-Joint Manipulators

    Directory of Open Access Journals (Sweden)

    Hicham Chaoui

    2013-05-01

    Full Text Available In this paper, we introduce an adaptive type-2 fuzzy logic controller (FLC for flexible-joint manipulators with structured and unstructured dynamical uncertainties. Simplified interval fuzzy sets are used for real-time efficiency, and internal stability is enhanced by adopting a trade-off strategy between the manipulator’s and the actuators’ velocities. Furthermore, the control scheme is independent of the computationally expensive noisy torque and acceleration signals. The controller is validated through a set of numerical simulations and by comparing it against its type-1 counterpart. The ability of the adaptive type-2 FLC in coping with large magnitudes of uncertainties yields an improved performance. The stability of the proposed control system is guaranteed using Lyapunov stability theory.

  12. Controlling chaos using Takagi-Sugeno fuzzy model and adaptive adjustment

    Institute of Scientific and Technical Information of China (English)

    Zheng Yong-Ai

    2006-01-01

    In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi-Sugeno (TS) fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on R(o)ssler's system verify the effectiveness of the proposed methods.

  13. 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.

  14. Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator

    Directory of Open Access Journals (Sweden)

    Amin Jalali

    2013-05-01

    Full Text Available The main purpose of this paper is to design a suitable control scheme that confronts the uncertainties in a robot. Sliding mode controller (SMC is one of the most important and powerful nonlinear robust controllers which has been applied to many non-linear systems. However, this controller has some intrinsic drawbacks, namely, the chattering phenomenon, equivalent dynamic formulation, and sensitivity to the noise. This paper focuses on applying artificial intelligence integrated with the sliding mode control theory. Proposed adaptive fuzzy sliding mode controller optimized by Particle swarm algorithm (AFSMC-PSO is a Mamdani’s error based fuzzy logic controller (FLS with 7 rules integrated with sliding mode framework to provide the adaptation in order to eliminate the high frequency oscillation (chattering and adjust the linear sliding surface slope in presence of many different disturbances and the best coefficients for the sliding surface were found by offline tuning Particle Swarm Optimization (PSO. Utilizing another fuzzy logic controller as an impressive manner to replace it with the equivalent dynamic part is the main goal to make the model free controller which compensate the unknown system dynamics parameters and obtain the desired control performance without exact information about the mathematical formulation of model.

  15. Artificial Error Tuning Based on Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control

    Directory of Open Access Journals (Sweden)

    Samaneh Zahmatkesh

    2013-10-01

    Full Text Available This paper examines single input single output (SISO chattering free variable structure control (VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm to control of continuum robot manipulator. Variable structure methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable error result and adjust the trajectory following. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and modified Proportional plus Derivative (P+D fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the modified rate of error. The outputs represent joint torque, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC based on-line tuned by fuzzy backstepping algorithm (FBSAVSC is validated through comparison with VSC. Simulation results signify good performance of trajectory in presence of uncertainty joint torque load.

  16. Identification and novel adaptive fuzzy control of nonlinear system for PEMFC stack

    Institute of Scientific and Technical Information of China (English)

    WEI Dong; XU Hong; ZHU Xin-jian

    2006-01-01

    The operating temperature of a proton exchange membrane fuel cell stack is a very important control parameter. It should be controlled within a specific range, however, most of existing PEMFC mathematical models are too complicated to be effectively applied to on-line control. In this paper, input-output data and operating experiences will be used to establish PEMFC stack model and operating temperature control system. An adaptive learning algorithm and a nearest-neighbor clustering algorithm are applied to regulate the parameters and fuzzy rules so that the model and the control system are able to obtain higher accuracy. In the end, the simulation and the experimental results are presented and compared with traditional PID and fuzzy control algorithms.

  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. Adaptive Neuro-Fuzzy Controller Experimental Design for DC Motor Connected to Unbalanced Load

    Directory of Open Access Journals (Sweden)

    Reza Nejati

    2007-09-01

    Full Text Available In two recent decades, fuzzy controllers have been used in controlling different systems successfully. In this article, a new method is given for controlling of permanent magnetic DC motor connected to unbalanced load. Imbalance of load leads to machine vibrations, fluctuation of power, making exhaustion in machine shaft, and equipment depreciation. In this article neuro-fuzzy controllers are used for controlling unbalanced load. Because of non-linear nature of load and machine, machine fluctuations are different in various speeds. For making controller adaptive with machine, using an artificial neural network, the input-output coefficients are be updated in any speed. Optimized coefficients obtained by using of direct search method, and with these coefficients, artificial neural network trained with Lauvenberg-Marcoardet method. Operational results obtained from developed system, shows the efficiency of given method.

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

    Directory of Open Access Journals (Sweden)

    Ali Moltajaei Farid

    2013-01-01

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

  20. L∞-gain adaptive fuzzy fault accommodation control design for nonlinear time-delay systems.

    Science.gov (United States)

    Wu, Huai-Ning; Qiang, Xiao-Hong; Guo, Lei

    2011-06-01

    In this paper, an adaptive fuzzy fault accommodation (FA) control design with a guaranteed L(∞)-gain performance is developed for a class of nonlinear time-delay systems with persistent bounded disturbances. Using the Lyapunov technique and the Razumikhin-type lemma, the existence condition of the L(∞) -gain adaptive fuzzy FA controllers is provided in terms of linear matrix inequalities (LMIs). In the proposed FA scheme, a fuzzy logic system is employed to approximate the unknown term in the derivative of the Lyapunov function due to the unknown fault function; a continuous-state feedback control strategy is adopted for the control design to avoid the undesirable chattering phenomenon. The resulting FA controllers can ensure that every response of the closed-loop system is uniformly ultimately bounded with a guaranteed L(∞)-gain performance in the presence of a fault. Moreover, by the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of the L(∞)-gain. Finally, the achieved simulation results on the FA control of a continuous stirred tank reactor (CSTR) show the effectiveness of the proposed design procedure.

  1. Benefits and challenges of controlling a LED AFS (Adaptive Front-lighting System) using fuzzy logic

    OpenAIRE

    2011-01-01

    Texto completo: acesso restrito. p.579−588 The vehicular illumination system has undergone considerable technological advances in recent decades such as the use of a Light Emitting Diode (LED) Adaptive Front-lighting System (AFS), which represents an industry breakthrough in lighting technology and is rapidly becoming one of the most important innovative technologies around the world in the lighting community. This paper presents AFS control alternatives using fuzzy logic (types 1...

  2. 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).

  3. Practical Velocity Tracking Control of a Parallel Robot Based on Fuzzy Adaptive Algorithm

    Directory of Open Access Journals (Sweden)

    Zude Zhou

    2013-01-01

    Full Text Available Due to the advantages of its compact structure and high operation accuracy, the six degrees of freedom (6-DOF parallel platform has been widely used as a carrier of medical rehabilitation devices. Fuzzy adaptive algorithm does not depend on the mathematical model of controlled object, which possesses good nonlinear characteristics. Those entire features make it an effective method to control such complex and coupling platforms. To facilitate the application of robotics in lower limb rehabilitation fields, a robotic system in practical environment was established based on kinematics modeling of the 6-DOF Stewart-based platform. In order to improve the velocity tracking accuracy, this paper proposed a closed-loop control strategy based on fuzzy adaptive algorithm. The velocity feedback information was utilized to modify the PID parameters adaptively in realtime through fuzzy inference units. Several experiments in practical environment were conducted, and the results demonstrated that the proposed algorithm could effectively reduce the speed jitter, enhance the position and velocity tracking precision of the robot, and the reliability and robustness of the system could also be ensured.

  4. 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-09-01

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

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

    Science.gov (United States)

    Luo, Shaohua

    2014-09-01

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

  7. Backstepping adaptive fuzzy control of uncertain nonlinear systems against actuator faults

    Institute of Scientific and Technical Information of China (English)

    Ping LI; Guanghong YANG

    2009-01-01

    A class of unknown nonlinear systems subject to uncertain actuator faults and external disturbances will be studied in this paper with the help of fuzzy approximation theory.Using backstepping technique,a novel adaptive fuzzy control approach is proposed to accommodate the uncertain actuator faults during operation and deal with the external disturbances though the systems cannot be linearized by feedback.The considered faults are modeled as both loss of effectiveness and lock-in-place (stuck at some unknown place).It is proved that the proposed control scheme can guarantee all signals of the closed-loop system to be semi-globally uniformly ultimately bounded and the tracking error between the system output and the reference signal converge to a small neighborhood of zero,though the nonlinear functions of the controlled system as well as the actuator faults and the external disturbances are all unknown.Simulation results demonstrate the effectiveness of the control approach.

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

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Shieh

    2014-04-01

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

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

    Directory of Open Access Journals (Sweden)

    Qin Li

    2014-12-01

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

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

    Institute of Scientific and Technical Information of China (English)

    Qin Li; Liu Fucai; Liang Lihuan; Gao Jingfang

    2014-01-01

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

  11. Chaos control using an adaptive fuzzy sliding mode controller with application to a nonlinear pendulum

    Energy Technology Data Exchange (ETDEWEB)

    Bessa, Wallace M. [Universidade Federal do Rio Grande do Norte, Department of Mechanical Engineering, Campus Universitario Lagoa Nova, 59072-970 Natal, RN (Brazil)], E-mail: wmbessa@ufrnet.br; Paula, Aline S. de [Universidade Federal do Rio de Janeiro, COPPE - Department of Mechanical Engineering, P.O. Box 68.503, 21941-972 Rio de Janeiro, RJ (Brazil)], E-mail: alinesp27@gmail.com; Savi, Marcelo A. [Universidade Federal do Rio de Janeiro, COPPE - Department of Mechanical Engineering, P.O. Box 68.503, 21941-972 Rio de Janeiro, RJ (Brazil)], E-mail: savi@mecanica.ufrj.br

    2009-10-30

    Chaos control may be understood as the use of tiny perturbations for the stabilization of unstable periodic orbits embedded in a chaotic attractor. The idea that chaotic behavior may be controlled by small perturbations of physical parameters allows this kind of behavior to be desirable in different applications. In this work, chaos control is performed employing a variable structure controller. The approach is based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm to cope with modeling inaccuracies. The convergence properties of the closed-loop system are analytically proven using Lyapunov's direct method and Barbalat's lemma. As an application of the control procedure, a nonlinear pendulum dynamics is investigated. Numerical results are presented in order to demonstrate the control system performance. A comparison between the stabilization of general orbits and unstable periodic orbits embedded in chaotic attractor is carried out showing that the chaos control can confer flexibility to the system by changing the response with low power consumption.

  12. Adaptive fuzzy control with output feedback for H infinity tracking of SISO nonlinear systems.

    Science.gov (United States)

    Rigatos, Gerasimos G

    2008-08-01

    Observer-based adaptive fuzzy H(infinity) control is proposed to achieve H(infinity) tracking performance for a class of nonlinear systems, which are subject to model uncertainty and external disturbances and in which only a measurement of the output is available. The key ideas in the design of the proposed controller are (i) to transform the nonlinear control problem into a regulation problem through suitable output feedback, (ii) to design a state observer for the estimation of the non-measurable elements of the system's state vector, (iii) to design neuro-fuzzy approximators that receive as inputs the parameters of the reconstructed state vector and give as output an estimation of the system's unknown dynamics, (iv) to use an H(infinity) control term for the compensation of external disturbances and modelling errors, (v) to use Lyapunov stability analysis in order to find the learning law for the neuro-fuzzy approximators, and a supervisory control term for disturbance and modelling error rejection. The control scheme is tested in the cart-pole balancing problem and in a DC-motor model.

  13. An adaptive fuzzy design for fault-tolerant control of MIMO nonlinear uncertain systems

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    This paper presents a novel control method for accommodating actuator faults in a class of multiple-input multiple-output (MIMO) nonlinear uncertain systems.The designed control scheme can tolerate both the time-varying lock-in-place and loss of effectiveness actuator faults.In each subsystem of the considered MIMO system,the controller is obtained from a backstepping procedure;an adaptive fuzzy approximator with minimal learning parameterization is employed to approximate the package of unknown nonlinear f...

  14. Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

    Institute of Scientific and Technical Information of China (English)

    Min WANG; Xiuying WANG; Bing CHEN; Shaocheng TONG

    2007-01-01

    In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.

  15. A Novel Fuzzy Logic Based Adaptive Super-Twisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    Directory of Open Access Journals (Sweden)

    Abdul Kareem

    2012-07-01

    Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.

  16. Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

    Science.gov (United States)

    Shi, Wuxi; Luo, Rui; Li, Baoquan

    2017-01-01

    In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results.

  17. Adaptive control design for a class of nonlinear systems based on fuzzy logic systems with scalers and saturators

    Science.gov (United States)

    Wang, Yin-He; Luo, Liang; Fan, Yong-Qing; Zhang, Yun; Liu, Xiao-Ping; Zhang, Si-Ying

    2014-03-01

    Many practical engineering applications require various types of fuzzy logic systems (FLSs) to design adaptive controllers for nonlinear systems with uncertainties. In this article, we will consider a fundamental theoretical question: is it possible to find a unified adaptive control design method suited to various types of FLSs? In order to solve this problem, we will introduce scalers and saturators at the input and output terminals of FLSs to form the extended FLSs (EFLS). The scalers and saturators have adjustable parameters. By designing the updated laws of these parameters and the estimate values of the fuzzy approximate accuracies, stable adaptive fuzzy controllers can be realised for a class of nonlinear systems with unknown homogeneous drift functions and gains. The proposed design method is only dependent on the outputs of EFLS and the above updated laws, thus increasing its adaptability. The fuzzy control scheme introduced in this article is suitable for all fuzzy systems with or without fuzzy rules. Simulations will also be used to show the validity of the method proposed in this article.

  18. Research on Fuzzy Immune Self-Adaptive PID Algorithm Based on New Smith Predictor for Networked Control System

    Directory of Open Access Journals (Sweden)

    Haitao Zhang

    2015-01-01

    Full Text Available We first analyze the effect of network-induced delay on the stability of networked control systems (NCSs. Then, aiming at stochastic characteristics of the time delay, we introduce a new Smith predictor to remove the exponential function with the time delay in the closed-loop characteristic equation of the NCS. Furthermore, we combine the fuzzy PID algorithm with the fuzzy immune control algorithm and present a fuzzy immune self-adaptive PID algorithm to compensate the influence of the model deviation of the controlled object. At last, a kind of fuzzy immune self-adaptive PID algorithm based on new Smith predictor is presented to apply to the NCS. The simulation research on a DC motor is given to show the effectiveness of the proposed algorithm.

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

    KAUST Repository

    Chaoui, Hicham

    2017-01-10

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

  20. Performance-Based Adaptive Fuzzy Tracking Control for Networked Industrial Processes.

    Science.gov (United States)

    Wang, Tong; Qiu, Jianbin; Yin, Shen; Gao, Huijun; Fan, Jialu; Chai, Tianyou

    2016-08-01

    In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system. Finally, a simulation example of continuous stirred tank reactor system is presented to show the effectiveness of the proposed control method.

  1. A Novel Fuzzy Logic Based Adaptive Super-Twisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

    Directory of Open Access Journals (Sweden)

    Abdul Kareem

    2012-08-01

    Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for thecontrol of dynamic uncertain systems. The proposed controller combines the advantages of Second orderSliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability androbustness of the system with the proposed controller are guaranteed. In addition, the proposed controlleris well suited for simple design and implementation. The effectiveness of the proposed controller over thefirst order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on aDC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desiredtransient response without causing chattering and error under steady-state conditions. The proposedcontroller is able to give robust performance in terms of rejection to input voltage variations and loadvariations

  2. Adaptive Controllers for Permanent Magnet Brushless DC Motor Drive System using Adaptive-Network-based Fuzzy Interference System

    Directory of Open Access Journals (Sweden)

    V. M. Varatharaju

    2011-01-01

    Full Text Available Problem statement: The tuning methodology for the parameters of adaptive speed controller causes a transient deviation of the response from the set reference following variation in load torque in a permanent-magnet brushless DC (BLDC motor drive system. Approach: This study develops a mathematical model of the BLDC drive system, firstly. Secondly, discusses a design of the closed loop drive system employing the Adaptive-Network-based Fuzzy Interference System (ANFIS. The nonlinear simulation model of the BLDC motors drive system with ANFIS control based is simulated in the MATLAB/SIMULINK platform. Results: The necessitated data for training the ANFIS control is generated by simulation of the system with conventional PI controller. Conclusion: The simulated electromagnetic torque and rotor speed signify the superiority of the proposed technique over the classical method.

  3. GA and Lyapunov theory-based hybrid adaptive fuzzy controller for non-linear systems

    Science.gov (United States)

    Roy, Ananya; Das Sharma, Kaushik

    2015-02-01

    In this present article, a new hybrid methodology for designing stable adaptive fuzzy logic controllers (AFLCs) for a class of non-linear system is proposed. The proposed design strategy exploits the features of genetic algorithm (GA)-based stochastic evolutionary global search technique and Lyapunov theory-based local adaptation scheme. The objective is to develop a methodology for designing AFLCs with optimised free parameters and guaranteed closed-loop stability. Simultaneously, the proposed method introduces automation in the design process. The stand-alone Lyapunov theory-based design, GA-based design and proposed hybrid GA-Lyapunov design methodologies are implemented for two benchmark non-linear plants in simulation case studies with different reference signals and one experimental case study. The results demonstrate that the hybrid design methodology outperforms the other control strategies on the whole.

  4. Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

    Science.gov (United States)

    Li, Jinsha; Li, Junmin

    2016-07-01

    In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

  5. Reconfigurable adaptive fuzzy fault-hiding control for greenhouse climate control system

    DEFF Research Database (Denmark)

    Hameed, Ibrahim; El-Madbouly, E I; Abdo, M I

    2017-01-01

    the recovery of closed-loop stability and the tracking of constant reference inputs. A configurable self-tuning state-feedback fuzzy-PI controller is used to correct faulty performance. The activeness of the proposed scheme has justified by simulation results on multi-input multi-output nonlinear greenhouse...

  6. Fuzzy Adaptive Repetitive Control for Periodic Disturbance with Its Application to High Performance Permanent Magnet Synchronous Motor Speed Servo Systems

    Directory of Open Access Journals (Sweden)

    Junxiao Wang

    2016-09-01

    Full Text Available For reducing the steady state speed ripple, especially in high performance speed servo system applications, the steady state precision is more and more important for real servo systems. This paper investigates the steady state speed ripple periodic disturbance problem for a permanent magnet synchronous motor (PMSM servo system; a fuzzy adaptive repetitive controller is designed in the speed loop based on repetitive control and fuzzy information theory for reducing periodic disturbance. Firstly, the various sources of the PMSM speed ripple problem are described and analyzed. Then, the mathematical model of PMSM is given. Subsequently, a fuzzy adaptive repetitive controller based on repetitive control and fuzzy logic control is designed for the PMSM speed servo system. In addition, the system stability analysis is also deduced. Finally, the simulation and experiment implementation are respectively based on the MATLAB/Simulink and TMS320F2808 of Texas instrument company, DSP (digital signal processor hardware platform. Comparing to the proportional integral (PI controller, simulation and experimental results show that the proposed fuzzy adaptive repetitive controller has better periodic disturbance rejection ability and higher steady state precision.

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

    Science.gov (United States)

    Savran, Aydogan; Kahraman, Gokalp

    2014-03-01

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

  8. 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-...

  9. 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.

  10. Adaptive Hybrid Fuzzy-Proportional Plus Crisp-Integral Current Control Algorithm for Shunt Active Power Filter Operation

    Directory of Open Access Journals (Sweden)

    Nor Farahaida Abdul Rahman

    2016-09-01

    Full Text Available An adaptive hybrid fuzzy-proportional plus crisp-integral current control algorithm (CCA for regulating supply current and enhancing the operation of a shunt active power filter (SAPF is presented. It introduces a unique integration of fuzzy-proportional (Fuzzy-P and crisp-integral (Crisp-I current controllers. The Fuzzy-P current controller is developed to perform gain tuning procedure and proportional control action. This controller inherits the simplest configuration; it is constructed using a single-input single-output fuzzy rule configuration. Thus, an execution of few fuzzy rules is sufficient for the controller’s operation. Furthermore, the fuzzy rule is developed using the relationship of currents only. Hence, it simplifies the controller development. Meanwhile, the Crisp-I current controller is developed to perform integral control action using a controllable gain value; to improve the steady-state control mechanism. The gain value is modified and controlled using the Fuzzy-P current controller’s output variable. Therefore, the gain value will continuously be adjusted at every sample period (or throughout the SAPF operation. The effectiveness of the proposed CCA in regulating supply current is validated in both simulation and experimental work. All results have proven that the SAPF using the proposed CCA is capable to regulate supply current during steady-state and dynamic-state operations. At the same time, the SAPF is able to enhance its operation in compensating harmonic currents and reactive power. Furthermore, the implementation of the proposed CCA has resulted more stable dc-link voltage waveform.

  11. Adaptive Fuzzy Tracking Control for Uncertain Nonlinear Time-Delay Systems with Unknown Dead-Zone Input

    Directory of Open Access Journals (Sweden)

    Chiang-Cheng Chiang

    2013-01-01

    Full Text Available The tracking control problem of uncertain nonlinear time-delay systems with unknown dead-zone input is tackled by a robust adaptive fuzzy control scheme. Because the nonlinear gain function and the uncertainties of the controlled system including matched and unmatched uncertainties are supposed to be unknown, fuzzy logic systems are employed to approximate the nonlinear gain function and the upper bounded functions of these uncertainties. Moreover, the upper bound of the uncertainty caused by the fuzzy modeling error is also estimated. According to these learning fuzzy models and some feasible adaptive laws, a robust adaptive fuzzy tracking controller is developed in this paper without constructing the dead-zone inverse. Based on the Lyapunov stability theorem, the proposed controller not only guarantees that the robust stability of the whole closed-loop system in the presence of uncertainties and unknown dead-zone input can be achieved, but it also obtains that the output tracking error can converge to a neighborhood of zero exponentially. Some simulation results are provided to demonstrate the effectiveness and performance of the proposed approach.

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

    Directory of Open Access Journals (Sweden)

    R. Razavi

    2007-01-01

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

  13. Adaptive Fuzzy Output-Feedback Stabilization Control for a Class of Switched Nonstrict-Feedback Nonlinear Systems.

    Science.gov (United States)

    Li, Yongming; Tong, Shaocheng

    2016-03-16

    This paper proposes an fuzzy adaptive output-feedback stabilization control method for nonstrict feedback uncertain switched nonlinear systems. The controlled system contains unmeasured states and unknown nonlinearities. First, a switched state observer is constructed in order to estimate the unmeasured states. Second, a variable separation approach is introduced to solve the problem of nonstrict feedback. Third, fuzzy logic systems are utilized to identify the unknown uncertainties, and an adaptive fuzzy output feedback stabilization controller is set up by exploiting the backstepping design principle. At last, by applying the average dwell time method and Lyapunov stability theory, it is proven that all the signals in the closed-loop switched system are bounded, and the system output converges to a small neighborhood of the origin. Two examples are given to further show the effectiveness of the proposed switched control approach.

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

    Directory of Open Access Journals (Sweden)

    Shun-Yuan Wang

    2015-03-01

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

  15. Decentralized adaptive fuzzy output feedback control of nonlinear interconnected systems with time-varying delay

    Science.gov (United States)

    Wang, Qin; Chen, Zuwen; Song, Aiguo

    2017-01-01

    A robust adaptive output-feedback control scheme based on K-filters is proposed for a class of nonlinear interconnected time-varying delay systems with immeasurable states. It is difficult to design the controller due to the existence of the immeasurable states and the time-delay couplings among interconnected subsystems. This difficulty is overcome by use of the fuzzy system, the K-filters and the appropriate Lyapunov-Krasovskii functional. Based on Lyapunov theory, the closed-loop control system is proved to be semi-global uniformly ultimately bounded (SGUUB), and the output tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the approach.

  16. Maximum Power Point Tracking Using Adaptive Fuzzy Logic control for Photovoltaic System

    Directory of Open Access Journals (Sweden)

    Anass Ait Laachir

    2015-01-01

    Full Text Available This work presents an intelligent approach to the improvement and optimization of control performance of a photovoltaic system with maximum power point tracking based on fuzzy logic control. This control was compared with the conventional control based on Perturb &Observe algorithm. The results obtained in Matlab/Simulink under different conditions show a marked improvement in the performance of fuzzy control MPPT of the PV system.

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

    DEFF Research Database (Denmark)

    Chen, Shuheng; Hu, Weihao; Su, Chi

    2015-01-01

    A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid PSO (FAHPSO) is proposed. The objective is to minimize comprehensive cost, consisting of power loss and operation cost of transformers...... and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages, maximum allowable daily switching operation number (MADSON). Particle swarm optimization (PSO) is used to solve the corresponding mixed integer non......-linear programming problem (MINLP) and the hybrid PSO method (HPSO), consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference based hybrid PSO is proposed. The proposed...

  18. Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems.

    Science.gov (United States)

    Li, Yongming; Tong, Shaocheng

    2016-08-25

    In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

  19. 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.

  20. Adaptive neuro-fuzzy logic analysis based on myoelectric signals for multifunction prosthesis control.

    Science.gov (United States)

    Favieiro, Gabriela W; Balbinot, Alexandre

    2011-01-01

    The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects.

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

    Directory of Open Access Journals (Sweden)

    Chen Siyu

    2017-01-01

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

  2. Speed Estimation of Adaptive Fuzzy-Controlled Piezo-Electric Motor using MLP-Neural Network

    Directory of Open Access Journals (Sweden)

    Shebel ALSABBAH

    2008-01-01

    Full Text Available The speed of ultrasonic motor of piezo-electric type is usually measured using mechanical sensors such as pulse encoders. However, these sensors are costly and bulky. In this paper, a numerical speed estimation approach of a piezo-electric motor (PEM is implemented using multi-layer perception neural network (MLP-NN. The proposed model evaluates rotational speed and load torque based on the amplitude and driving frequency of the terminal voltage, considering the temperature variation. The estimated speed is employed to enhance the performance of the adaptive-fuzzy based speed control system. The model is validated and examined to achieve a minimized relative error in speed estimation approaches.

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

    Directory of Open Access Journals (Sweden)

    Huanqing Wang

    2014-01-01

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

  4. Flatness-based adaptive fuzzy control of an autonomous submarine model

    Science.gov (United States)

    Rigatos, Gerasimos; Siano, Pierluigi; Raffo, Guilherme

    2015-12-01

    The article presents a differential flatness theory-based method for adaptive control of autonomous submarines. A proof is provided about the differential flatness properties of the submarine's model (having as state variables the vessel's depth and its pitch angle). This also means that all its state variables and its control inputs can be written as differential functions of the flat output. Making use of its differential flatness features, the submarine's dynamic model is transformed into the multivariable linear canonical (Brunovsky) form. In the transformed model, the control inputs consist of unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning rate for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Furthermore, with the use of Lyapunov stability analysis it is proven that an H-infinity tracking performance is succeeded for the feedback control loop. This implies enhanced robustness to model uncertainty and to external perturbations. Simulation experiments are carried out to further confirm the efficiency of the proposed adaptive fuzzy control scheme.

  5. 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.

  6. Robust Adaptive Fuzzy Design for Ship Linear-tracking Control with Input Saturation

    Directory of Open Access Journals (Sweden)

    Yancai Hu

    2017-04-01

    Full Text Available A robust adaptive control approach is proposed for underactuated surface ship linear path-tracking control system based on the backstepping control method and Lyapunov stability theory. By employing T-S fuzzy system to approximate nonlinear uncertainties of the control system, the proposed scheme is developed by combining “dynamic surface control” (DSC and “minimal learning parameter” (MLP techniques. The substantial problems of “explosion of complexity” and “dimension curse” existed in the traditional backstepping technique are circumvented, and it is convenient to implement in applications. In addition, an auxiliary system is developed to deal with the effect of input saturation constraints. The control algorithm avoids the singularity problem of controller and guarantees the stability of the closed-loop system. The tracking error converges to an arbitrarily small neighborhood. Finally, MATLAB simulation results are given from an application case of Dalian Maritime University training ship to demonstrate the effectiveness of the proposed scheme.

  7. Robust Adaptive Fuzzy Control for Planetary Rovers While Climbing up Deformable Slopes with Longitudinal Slip

    Directory of Open Access Journals (Sweden)

    Li Zhengcai

    2014-01-01

    Full Text Available Mobility control is one of the most essential parts of planetary rovers’ research and development. The goal of this research is to let the planetary rovers be able to achieve demand of motion from upper level with satisfied control performance under the rough and deformable planetary terrain that often lead to longitudinal slip. The longitudinal slip influences the mobility efficiency obviously, especially on the major deformable slopes. Compared with the past works on normal stiff terrains, properties of soil and interaction between wheels and soil should be considered additionally. Therefore, to achieve the final goal, in this paper, wheel-soil dynamic model for six-wheel planetary rovers while climbing up deformable slopes with longitudinal slip is first built and control based in order to account for slip phenomena. These latter effects are then taken into account within terramechanics theory, relying upon nonlinear control techniques; finally, a robust adaptive fuzzy control strategy with longitudinal slip compensation is developed to reduce the effects induced by slip phenomena and modeling error. Capabilities of this control scheme are demonstrated via full scale simulations carried out with a six-wheel robot moving on sloped deformable terrain, whose real time was computed relying uniquely upon RoSTDyn, a dynamic software.

  8. Based on interval type-2 adaptive fuzzy H∞ tracking controller for SISO time-delay nonlinear systems

    Science.gov (United States)

    Lin, Tsung-Chih; Roopaei, Mehdi

    2010-12-01

    In this article, based on the adaptive interval type-2 fuzzy logic, by adjusting weights, centers and widths of proposed fuzzy neural network (FNN), the modeling errors can be eliminated for a class of SISO time-delay nonlinear systems. The proposed scheme has the advantage that can guarantee the H∞ tracking performance to attenuate the lumped uncertainties caused by the unmodelled dynamics, the approximation error and the external disturbances. Moreover, the stability analysis of the proposed control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded and arbitrary small attenuation level. The simulation results are demonstrated to show the effectiveness of the advocated design methodology.

  9. Characterization and adaptive fuzzy model reference control for a magnetic levitation system

    Directory of Open Access Journals (Sweden)

    J.J. Hernández-Casañas

    2016-09-01

    Full Text Available This paper shows the implementation of a fuzzy controller applied for magnetic levitation, to make this, the characterization of the magnetic actuator was computed by using ANSYS® analysis. The control law was a Mamdani PD implemented with two microcontrollers, to get a smooth control signal, it was used a model reference. A learning scheme was used to update the consequents of the fuzzy rules. Different reference signals and disturbances were applied to the system to show the robustness of the controller. Finally, LabVIEW® was used to plot the results.

  10. Adaptive fuzzy control with smooth inverse for nonlinear systems preceded by non-symmetric dead-zone

    Science.gov (United States)

    Wang, Xingjian; Wang, Shaoping

    2016-07-01

    In this study, the adaptive output feedback control problem of a class of nonlinear systems preceded by non-symmetric dead-zone is considered. To cope with the possible control signal chattering phenomenon which is caused by non-smooth dead-zone inverse, a new smooth inverse is proposed for non-symmetric dead-zone compensation. For the systematic design procedure of the adaptive fuzzy control algorithm, we combine the backstepping technique and small-gain approach. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown system nonlinearities. The closed-loop stability is studied by using small gain theorem and the closed-loop system is proved to be semi-globally uniformly ultimately bounded. Simulation results indicate that, compared to the algorithm with the non-smooth inverse, the proposed control strategy can achieve better tracking performance and the chattering phenomenon can be avoided effectively.

  11. Fuzzy logic-based diversity-controlled self-adaptive differential evolution

    Science.gov (United States)

    Amali, S. Miruna Joe; Baskar, S.

    2013-08-01

    This article presents a novel method using a fuzzy system (FS) to control the population diversity during the various phases of evolution. A local search is applied at regular intervals on an individual selected at random to aid the population in convergence. This diversity control methodology is applied to vary the crossover rate of self-adaptive differential evolution (SaDE). Three variants of the SaDE algorithm are proposed: (1) diversity-controlled SaDE (DCSaDE); (2) SaDE with local search (SaDE-LS); and (3) diversity-controlled SaDE with local search (DCSaDE-LS). The performance of the proposed algorithms is analysed using a set of unconstrained benchmark functions with respect to average function evaluations, success rate and the mean of the objectives of 30 independent trials. The DCSaDE-LS algorithm had a better success rate for high-dimensional multimodal problems and conserved the number of function evaluations required for most of the problems. It is compared with other popular algorithms and the outcome of the proposed DCSaDE-LS algorithm is validated using non-parametric statistical tests. MATLAB codes for the proposed algorithms may be obtained on request.

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

    Directory of Open Access Journals (Sweden)

    YUCEL, M.

    2017-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Maryam Montazeri

    2013-01-01

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

  14. Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

    Institute of Scientific and Technical Information of China (English)

    Thang-Long MAI; Yaonan WANG

    2014-01-01

    In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control (ABFCNC) system for motion/force control of the mobile-manipulator robot (MMR) is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomic-constraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.

  15. Adaptive fuzzy PID temperature control system based on single-chip computer for the autoclave

    Science.gov (United States)

    Zhang, F.; Wang, J.; Fu, S. L.; He, Z. T.; Li, X. P.

    2008-12-01

    The autoclave is one of main preparation equipments of crystal preparation by hydrothermal method. The preparation temperature will seriously influence crystals quality and crystals size at high temperature, how to measure and control precisely the autoclave temperature can be of real significance. The characteristic of hysteresis, nonlinearity and difficulty to acquire the precise mathematical model existing in the temperature control of the autoclave was researched. The general PID controller adopted usually in the autoclave temperature control system is hard to improve temperature control performance. Based on the advantages of fuzzy controller that does not depend on the precise mathematical model and the stabilization of PID controller, single-chip computer integrated fuzzy PID control algorithm is adopted, and the temperature system is designed, the foundational working principle was discussed. The control system includes SCM (AT89C52), temperature sensor, A/D converter circuit and corresponding circuit and interface, can make the autoclave temperature measure and control accurately. The system hardware includes main circuit, thyristor drive circuit, audible and visual alarm circuit, watchdog circuit, clock circuit, keyboard and display circuit so on, which can achieve gathering, analyzing, comparing and controlling the autoclave temperature parameter. The program of control system includes the treatment and collection of temperature data, the dynamic display program, the fuzzy PID control system, the audible and visual alarm program, et al, and the system's main software, which includes initialization, key-press processing, input processing, display, and the fuzzy PID control program was analyzed. The results showed that the fuzzy PID control system makes the adjustment time of temperature decreased and the precision of temperature control improved, the quality and the crystals size of the preparation crystals can achieve the expect experiment results.

  16. Fuzzy Control Strategies in Human Operator and Sport Modeling

    CERN Document Server

    Ivancevic, Tijana T; Markovic, Sasa

    2009-01-01

    The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal control. As an application of the presented fuzzy strategies, we present a fuzzy-control based tennis simulator.

  17. Active pneumatic vibration control by using pressure and velocity measurements and adaptive fuzzy sliding-mode controller.

    Science.gov (United States)

    Chen, Hung-Yi; Liang, Jin-Wei; Wu, Jia-Wei

    2013-07-02

    This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI) system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system's nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control.

  18. Active Pneumatic Vibration Control by Using Pressure and Velocity Measurements and Adaptive Fuzzy Sliding-Mode Controller

    Directory of Open Access Journals (Sweden)

    Jia-Wei Wu

    2013-07-01

    Full Text Available This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system’s nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control.

  19. Adaptive fuzzy output-feedback controller design for nonlinear time-delay systems with unknown control direction.

    Science.gov (United States)

    Hua, Chang-Chun; Wang, Qing-Guo; Guan, Xin-Ping

    2009-04-01

    In this paper, the robust-control problem is investigated for a class of uncertain nonlinear time-delay systems via dynamic output-feedback approach. The considered system is in the strict-feedback form with unknown control direction. A full-order observer is constructed with the gains computed via linear matrix inequality at first. Then, with the bounds of uncertain functions known, we design the dynamic output-feedback controller such that the closed-loop system is asymptotically stable. Furthermore, when the bound functions of uncertainties are not available, the adaptive fuzzy-logic system is employed to approximate the uncertain function, and the corresponding output-feedback controller is designed. It is shown that the resulting closed-loop system is stable in the sense of semiglobal uniform ultimate boundedness. Finally, simulations are done to verify the feasibility and effectiveness of the obtained theoretical results.

  20. 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....

  1. 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 t......, 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....

  2. Design of Fuzzy Controllers

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    Design of a fuzzy controller requires more design decisions than usual, for example regarding rule base, inference engine, defuzzification, and data pre- and post processing. This tutorial paper identifies and describes the design choices related to single-loop fuzzy control, based...... on an international standard which is underway. The paper contains also a design approach, which uses a PID controller as a starting point. A design engineer can view the paper as an introduction to fuzzy controller design....

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

    Science.gov (United States)

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

    2015-08-01

    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.

  4. Sequential Adaptive RBF-Fuzzy Variable Structure Control Applied to Robotics Systems

    Directory of Open Access Journals (Sweden)

    Mohammed Salem

    2014-08-01

    Full Text Available In this paper, we present a combination of sequential trained radial basis function networks and fuzzy techniques to enhance the variable structure controllers dedicated to robotics systems. In this aim, four RBFs networks were used to estimate the model based part parameters (Inertia, Centrifugal and Coriolis, Gravity and Friction matrices of a variable structure controller so to respond to model variation and disturbances, a sequential online training algorithm based on Growing-Pruning "GAP" strategy and Kalman filter was implemented. To eliminate the chattering effect, the corrective control of the VS control was computed by a fuzzy controller. Simulations are carried out to control three degrees of freedom SCARA robot manipulator where the obtained results show good disturbance rejection and chattering elimination.

  5. Indirect Adaptive Fuzzy Output Feedback Control with Supervisory Controller for Uncertain Nonlinear Systems%非线性系统的间接自适应模糊输出反馈监督控制

    Institute of Scientific and Technical Information of China (English)

    佟绍成; 柴天佑

    2005-01-01

    In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be de-activated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.

  6. Active fault tolerant control based on interval type-2 fuzzy sliding mode controller and non linear adaptive observer for 3-DOF laboratory helicopter.

    Science.gov (United States)

    Zeghlache, Samir; Benslimane, Tarak; Bouguerra, Abderrahmen

    2017-09-14

    In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller. Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Investigation on Complex Fuzzy Controller Adaptability%复合模糊控制器适应性研究

    Institute of Scientific and Technical Information of China (English)

    林碧华; 韩璞; 赵艳

    2001-01-01

    This paper shows how to connect fuzzy control with PID contorl and introduces three methods of connecting Fuzzy control with PID control, PID-Fuzzy Control, Fuzzy-PID Control and PID-Fuzzy-PID Control.With the simulation results, their features and application conditions are illustrated.%阐述了Fuzzy控制与PID控制相结合的基本思想,介绍了Fuzzy控制与PID控制相结合的3种方法:PID-Fuzzy控制、Fuzzy-PID控制、PID-Fuzzy-PID控制,并做了大量仿真,通过仿真,论述了这3种方法各自的优缺点及各自适用的系统。

  8. Robust DTC Based on Adaptive Fuzzy Control of Double Star Synchronous Machine Drive with Fixed Switching Frequency

    Science.gov (United States)

    Boudana, Djamel; Nezli, Lazhari; Tlemçani, Abdelhalim; Mahmoudi, Mohand Oulhadj; Tadjine, Mohamed

    2012-05-01

    The double star synchronous machine (DSSM) is widely used for high power traction drives. It possesses several advantages over the conventional three phase machine. To reduce the torque ripple the DSSM are supplied with source voltage inverter (VSI). The model of the system DSSM-VSI is high order, multivariable and nonlinear. Further, big harmonic currents are generated. The aim of this paper is to develop a new direct torque adaptive fuzzy logic control in order to control DSSM and minimize the harmonics currents. Simulations results are given to show the effectiveness of our approach.

  9. Adaptive fuzzy output-feedback controller design for nonlinear systems via backstepping and small-gain approach.

    Science.gov (United States)

    Liu, Zhi; Wang, Fang; Zhang, Yun; Chen, Xin; Chen, C L Philip

    2014-10-01

    This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.

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

    Directory of Open Access Journals (Sweden)

    Peng Fei Wang

    2016-10-01

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

  11. Active Vibration Suppression of a Motor-Driven Piezoelectric Smart Structure Using Adaptive Fuzzy Sliding Mode Control and Repetitive Control

    Directory of Open Access Journals (Sweden)

    Chi-Ying Lin

    2017-03-01

    Full Text Available In this paper, we report on the use of piezoelectric sensors and actuators for the active suppression of vibrations associated with the motor-driven rotation of thin flexible plate held vertically. Motor-driven flexible structures are multi-input multi-output systems. The design of active vibration-suppression controllers for these systems is far more challenging than for flexible structures with a fixed end, due to the effects of coupling and nonlinear vibration behavior generated in structures with poor damping. To simplify the design of the controller and achieve satisfactory vibration suppression, we treated the coupling of vibrations caused by the rotary motion of the thin flexible plate as external disturbances and system uncertainties. We employed an adaptive fuzzy sliding mode control algorithm in the design of a single-input–single-output controller for the suppression of vibrations using piezoelectric sensors and actuators. We also used a repetitive control system to reduce periodic vibrations associated with the repetitive motions induced by the motor. Experimental results demonstrate that the hybrid intelligent control approach proposed in this study can suppress complex vibrations caused by modal excitation, coupling effects, and periodic external disturbances.

  12. 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. .

  13. Fuzzy-Approximator-Based Adaptive Controller Design for Ship Course-Keeping Steering in Strict-Feedback Forms

    Directory of Open Access Journals (Sweden)

    Junsheng Ren

    2013-09-01

    Full Text Available Along with increasing marine transportation and logistics, the ship autopilot has become much important not only to lower the seaman's operating intensions, but also to reduce the seaman's deployment. It is still a challenge to design ship course-keeping controller because of ship's uncertain dynamics and time-varying environmental disturbance. This study focuses on backstepping adaptive course-keeping controller design for ship autopilot. Takagi-Sugeno (T-S fuzzy approximator can formulate ship motion's uncertainties. Therefore, the proposed controller has no need of a priori knowledge about ship's system dynamics. Command filter can bypass the iterative differential manipulations in conventional ship course adaptive backstepping controller. The design can guarantee the ultimate uniform boundedness of the signals in closed-loop system. Finally, simulation study verifies the efficiency of the ship course-keeping design.

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

    Directory of Open Access Journals (Sweden)

    Otilia Elena Dragomir

    2015-11-01

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

  15. Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot.

    Science.gov (United States)

    Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar

    2016-09-09

    A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm.

  16. Fuzzy Supervisory Control

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    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. For high level control and supervi......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. For high level control...

  17. Research of soft startup control system for induction motor based on fuzzy adaptive control%感应电动机智能软启动器研究

    Institute of Scientific and Technical Information of China (English)

    薛晓; 李昊伦

    2012-01-01

    In order to solve the large startup current of induction motors, a fuzzy adaptive control soft start system is presented in this paper. It can control startup current and avoid the inrush of the current for smooth running. According to the characteristic of induction motor, taking current error and error change rate as input quantity and the control angle of thyristor as output quantity, the fuzzy A-daptive control algorithm for the soft startup of induction motors was designed by using fuzzy logical toolbox in Matlab software. In comparison with the traditional PID and traditional fuzzy control methods, the self-adaptive fuzzy control method is more superior.%为了解决三相异步电动机起动电流较大的问题,采用了一种软起动模糊自适应控制器.据其起动特性,将电流误差和误差的变化率作为输入量,晶闸管的控制角作为输出量,设计出一种自调整比例因子的模糊自适应控制算法.随后做了Matlab环境下的仿真实验,并且与常规的PID控制方法和常规模糊控制方法进行比较,得到了模糊自适应控制算法具有优越性的结论.

  18. Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems

    Directory of Open Access Journals (Sweden)

    A. Aldair

    2010-12-01

    Full Text Available The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIλ Dμ (FOPID controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

  19. 基于模糊控制的自适应光学校正技术∗%Adaptive optics correction technique based on fuzzy control

    Institute of Scientific and Technical Information of China (English)

    刘章文; 李正东; 周志强; 袁学文

    2016-01-01

    In an adaptive optics system, proportion-integration-differentiation (PID) controller is widely used for correcting wave front, but the controller is strictly dependent on the response model of deformable mirror. In this paper, a novel wave front correction method is proposed. The method, combining fuzzy control and PID control, does not depend on the response model of the deformable mirror. Based on rapid wave front reconstruction, the wave front evaluation indexes, extracted from the reconstructed wave front, are employed for the input of fuzzy controller and PID controller. Thus, the model response matrix of deformable mirror is not required. Each actuator of deformable mirror corresponds to an independent fuzzy PID controller. By designing the fuzzy controller, including fuzzy rule base selection and fuzzy reasoning, the three parameters of PID controller, the proportional kp, the integral ki and the differential kd, are adjusted automatically. A high rapid DSP hardware platform is constructed to verify the method. Test results show that the method can be used to correct the diffraction limit multiplication factorβ of the light spot from 10–12 to 3–4, which is basically the same as the traditional PID control, but its stability is better. Because the model does not need to calibrate the deformable mirror, the installation of the deformable mirror is easier.

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

    Energy Technology Data Exchange (ETDEWEB)

    Rojas R, E.

    2014-07-01

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

  1. Design Fuzzy Input-Based Adaptive Sliding Mode Control for Vessel Lift-Feedback Fin Stabilizers with Shock and Vibration of Waves

    Directory of Open Access Journals (Sweden)

    Lihua Liang

    2017-01-01

    Full Text Available An adaptive sliding mode controller based on fuzzy input design is presented, in order to reduce the roll motion of surface vessel fin stabilizers with shock and vibration of waves. The nonlinearities and uncertainties of the system including feedback errors and disturbance induced by waves are analyzed. And the lift-feedback system is proposed, which improves the shortage of conventional fin angle-feedback. Then the fuzzy input-based adaptive sliding mode control is designed for the system. In the controller design, the Lyapunov function is adopted to guarantee the system stability. Finally, experimental results demonstrate the superior performance of the controller designed using fuzzy input, when compared to the PID controller used in practical engineering.

  2. 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 ... an algorithm that allows a designer to initially specify a possibly inaccurate rule-base, which ... an adaptive FLC strategy based on these ideas.

  3. Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS.

    Science.gov (United States)

    Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu

    2014-09-01

    This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations. Copyright © 2014. Published by Elsevier Ltd.

  4. Adaptive Fuzzy Control for Power-Frequency Characteristic Regulation in High-RES Power Systems

    Directory of Open Access Journals (Sweden)

    Evangelos Rikos

    2017-07-01

    Full Text Available Future power systems control will require large-scale activation of reserves at distribution level. Despite their high potential, distributed energy resources (DER used for frequency control pose challenges due to unpredictability, grid bottlenecks, etc. To deal with these issues, this study presents a novel strategy of power frequency characteristic dynamic adjustment based on the imbalance state. This way, the concerned operators become aware of the imbalance location but also a more accurate redistribution of responsibilities in terms of reserves activations is achieved. The proposed control is based on the concept of “cells” which are power systems with operating capabilities and responsibilities similar to control areas (CAs, but fostering the use of resources at all voltage levels, particularly distribution grids. Control autonomy of cells allows increased RES hosting. In this study, the power frequency characteristic of a cell is adjusted in real time by means of a fuzzy controller, which curtails part of the reserves, in order to avoid unnecessary deployment throughout a synchronous area, leading to a more localised activation and reducing losses, congestions and reserves exhaustion. Simulation tests in a four-cell reference power system prove that the controller significantly reduces the use of reserves without compromising the overall stability.

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

    Directory of Open Access Journals (Sweden)

    Zhixian Yang

    2014-01-01

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

  6. ACS algorithm-based adaptive fuzzy PID controller and its application to CIP-Ⅰ intelligent leg

    Institute of Scientific and Technical Information of China (English)

    TAN Guan-zheng; DOU Hong-quan

    2007-01-01

    Based on the ant colony system (ACS) algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed controller, called the Fuzzy-ACS PID controller, was used to control the CIP-Ⅰ intelligent leg. The simulation experiments demonstrate that this controller has good control performance. Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing, it was verified that the Fuzzy-ACS PID controller has better control performance. Furthermore, the simulation results also verify that the proposed ACS algorithm has quick convergence speed, small solution variation, good dynamic convergence behavior, and high computation efficiency in searching for the optimal input/output scaling factors.

  7. Tuning of Fuzzy PID Controllers

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains compared to proportional-integral-derivative (PID) controllers. This research paper proposes a design procedure and a tuning procedure that carries tuning rules from the PID domain over to fuzzy single......-loop controllers. The idea is to start with a tuned, conventional PID controller, replace it with an equivalent linear fuzzy controller, make the fuzzy controller nonlinear, and eventually fine-tune the nonlinear fuzzy controller. This is relevant whenever a PID controller is possible or already implemented....

  8. Using an Adaptative 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)

    M. M. Krishan

    2010-01-01

    Full Text Available Problem statement: 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 mo Approach: Motivation behind the use of neuro-fuzzy approaches was 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, ensure more robustness of the overall system 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. Results: In fact, the aim of such a research consists first in simplifying the control of the motor by decoupling between two principles variables which provoque the torque in the motor by using the feedback linearization method. Then, using sliding mode controllers to give our process more robustness towards the variation of different parameters of the motor. However, the latter technique of control called sliding mode control caused an indesirable phenomenon which harmful and could leads to the deterioration of the inverters components called chattering. So, here the authors propose to use neuro-fuzzy systems to reduce this phenomenon and perform the performances of the adopted control process. The type of the neuro-fuzzy system used here is called: Adaptive Neuro Fuzzy Inference System (ANFIS. This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Conclusion: Therefore, from a control design consideration, the adopted neuro-fuzzy system has opened up a new

  9. Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks

    Directory of Open Access Journals (Sweden)

    Vicente Hernández Díaz

    2015-09-01

    Full Text Available The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT and Cyber-Physical Systems (CPS are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container, and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

  10. Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks.

    Science.gov (United States)

    Hernández Díaz, Vicente; Martínez, José-Fernán; Lucas Martínez, Néstor; del Toro, Raúl M

    2015-09-18

    The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-10-27

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

  12. Research on Fuzzy Control for Automatic Transmission of Tracked Vehicles

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A principle of fuzzy control for tracked vehicles is proposed to make its automatic transmission system be able to adapt complex running conditions, and a model of its power train is established to be used in simulation. Based on the fuzzy control method, a fuzzy shift control system composed of a basic shift strategy and a fuzzy modification module is developed to improve the dynamic characteristics and cross-country maneuverability. Simulation results show that the fuzzy shift strategy can improve the shift quality under manifold driving conditions and avoid cycled shift effectively. Therefore,the proposed fuzzy shift strategies are proved to be feasible and practicable.

  13. A New Neuro-Fuzzy Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHU Lili; ZHANG Huanchun; JING Yazhi

    2003-01-01

    Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and tuning the fuzzy knowledge-base system, neurofuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learning method. The Results from initial experiments show a Dynamic Parametric AGA system designed by the proposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a wide range of combinatorial optimization.

  14. 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.

  15. Brushless DC Motor Self-Adaption Fuzzy PID Control System%无刷直流电机自适应模糊PID控制系统

    Institute of Scientific and Technical Information of China (English)

    王国玲; 李振宇; 范自道

    2013-01-01

      针对无刷直流电机传统PID控制存在精度低、抗干扰能力差及模糊控制稳态精度不高等问题,研究了一种自适应模糊PID控制方法。论文分析了直流无刷电机的工作原理,建立了直流无刷电机自适应模糊PID控制系统的计算机仿真数学模型,设计了系统速度环的模糊PID控制器,仿真结果表明,与传统PID控制相比,自适应模糊PID控制的BLDCM系统具有更高的稳定性和控制精度、更快的动态响应速度。%For the lower precision,bad anti-interference capability of traditional PID control and lower stable precision of fuzzy control for brushless DC motor,an adaptive fuzzy-PID control was deeply investigated in this paper. Working theory of brushless DC motor was analyzed,the simulated mathematical model of adaptive fuzzy-PID control for brushless DC motor was established,and the fuzzy-PID controller of speed regulator was designed. The results of simulation verified the better stability,the faster dynamic speed of adaptive fuzzy-PID control for brushless DC motor compared to traditional PID control.

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

    Science.gov (United States)

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

    2011-04-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-07-15

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

  18. Relationship between fuzzy controllers and PID controllers

    Institute of Scientific and Technical Information of China (English)

    李洪兴

    1999-01-01

    The internal relations between fuzzy controllers and PID controllers are revealed. First, it is pointed out that a fuzzy controller with one input and one output is just a piecewise P controller. Then it is proved that a fuzzy controller with two inputs and one output is just a piecewise PD (or I) controller with interaction between P and D (or PI). At last, the conclusion that a fuzzy controller with three inputs and one output is just a piecewise PID controller with interaction among P, I and D is given. Moreover, a kind of difference scheme of fuzzy controllers is designed.

  19. fuzzy control technique fuzzy control technique applied to modified ...

    African Journals Online (AJOL)

    eobe

    ABSTRACT. In this paper, fuzzy control technique is applied to the modified mathematical model for malaria control presented ... be devised for rule-based systems that deals with continuous ... necessary to use fuzzy logic as it is not easy to follow a particular .... point movement and control is realized and designed. (e.g. α1 ...

  20. HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

    Science.gov (United States)

    Kim, J; Kasabov, N

    1999-11-01

    This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

  1. A Load Frequency Control in an Off-Grid Sustainable Power System Based on a Parameter Adaptive PID-Type Fuzzy Controller

    Science.gov (United States)

    Ronilaya, Ferdian; Miyauchi, Hajime

    2014-10-01

    This paper presents a new implementation of a parameter adaptive PID-type fuzzy controller (PAPIDfc) for a grid-supporting inverter of battery to alleviate frequency fluctuations in a wind-diesel power system. A variable speed wind turbine that drives a permanent magnet synchronous generator is assumed for demonstrations. The PAPIDfc controller is built from a set of control rules that adopts the droop method and uses only locally measurable frequency signal. The output control signal is determined from the knowledge base and the fuzzy inference. The input-derivative gain and the output-integral gain of the PAPIDfc are tuned online. To ensure safe battery operating limits, we also propose a protection scheme called intelligent battery protection (IBP). Several simulation experiments are performed by using MATLAB®/SimPowersystems™. Next, to verify the scheme's effectiveness, the simulation results are compared with the results of conventional controllers. The results demonstrate the effectiveness of the PAPIDfc scheme to control a grid-supporting inverter of the battery in the reduction of frequency fluctuations.

  2. Aircraft Attitude Control by Fuzzy Control

    Science.gov (United States)

    Kato, Akio; Matsuba, Takashi

    The fuzzy control law to improve dutch roll characteristics of aircraft was designed and its control performance was evaluated. First, the control law was designed for a small-high speed aircraft at low altitude and low-speed flight conditions. The control law was then applied to flight conditions from minimum speed to supersonic speed and from sea level to high altitude. The control performance for these conditions was evaluated. Furthermore, this control law was adapted to a large transport aircraft with no parameter changes. The evaluation showed good control performance to improve the dutch roll characteristics under all flight conditions for both small high-speed aircraft and large transport aircraft without the parameter changes. This means that the fuzzy control proved to provide effective flexible application to aircraft stability augmentation. If an aircraft in actual flight is in strong air turbulence, inputs to the fuzzy controller may exceed the limit of its effective range. To cope with this problem, the countermeasures were introduced, their methods tested, and their effectiveness proved.

  3. Fuzzy Logic Control ASIC Chip

    Institute of Scientific and Technical Information of China (English)

    沈理

    1997-01-01

    A fuzzy logic control VLSI chip,F100,for industry process real-time control has been designed and fabricated with 0.8μm CMOS technology.The chip has the features of simplicity,felexibility and generality.This paper presents the Fuzzy control inrerence method of the chip,its VLSI implementation,and testing esign consideration.

  4. Fuzzy cascade control based on control's history for superheated temperature

    Institute of Scientific and Technical Information of China (English)

    WANG Guangjun; LI Gang; SHEN Shuguang

    2007-01-01

    To address the characteristics of the large delay and uncertainty of superheated temperature,a new cascade control system is presented based on control's history.Based on the analysis of the control objects' dynamic characteristics,historical control information (substituting for the deviation change rate) is used as the basis for decision-making of the fuzzy control.Therefore,the changing trend of the controlled variable can be accurately reflected.Furthermore,a proportional component is introduced,the advantages of PID and fuzzy controllers are integrated,and the structure weaknesses of conventional fuzzy controllers are overcome.Simulation shows that this control method can effectively reduce the adverse impact of the delay on control effects and,therefore,exhibit strong adaptability by comparing the superheated temperature control system by this controller with PID and conventional fuzzy controllers.

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

    Science.gov (United States)

    Ajay Kumar, M.; Srikanth, N.

    2014-03-01

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

  6. 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...

  7. Innovative Strategy to Improve Precision and to Save Power of a Real-Time Control Process Using an Online Adaptive Fuzzy Logic Controller

    Directory of Open Access Journals (Sweden)

    R. Lasri

    2013-01-01

    Full Text Available The main objective of this paper is to prove the great advantage that brings our novel approach to the intelligent control area. A set of various types of intelligent controllers have been designed to control the temperature of a room in a real-time control process in order to compare the obtained results with each other. Through a training board that allows us to control the temperature, all the used algorithms should present their best performances in this control process; therefore, our self-organized and online adaptive fuzzy logic controller (FLC will be required to present great improvements in the control task and a real high control performance. Simulation results can show clearly that the new approach presented and tested in this work is very efficient. Thus, our adaptive and self-organizing FLC presents the best accuracy compared with the remaining used controllers, and, besides that, it can guarantee an important reduction of the power consumption during the control process.

  8. 基于Niche的间接T-S模糊自适应控制%Indirect adaptive control of T-S fuzzy systems based on Niche

    Institute of Scientific and Technical Information of China (English)

    李医民; 郝云力

    2011-01-01

    提出了新的基于生态系统的Niche间接T-S(Takagi-Sugeno)自适应模糊控制,将含有参数的生态位贴近度函数作为模糊规则的后件,从而构成零阶的T-S模糊控制模型.采用Lyapunov合成方法设计控制器,并使用梯度下降法优化后件参数,最后得到了后件参数的自适应律,体现了生物个体的自适应能力,引入了生态位的模糊系统具有生物个体始终朝着有利于自身方向发展的特性.因此,所建立的模糊T-S系统具有更好的自适应性,可以获得更小的变动跟踪误差,并保证整个闭环系统全局稳定性.通过对生态系统的二维捕食系统的仿真验证了本方法的可行性.%A new indirect T-S (Takagi-Sugeno) adaptive fuzzy control based on niche of ecosystems is presented. Regarding the approach degree function of niche with unknown parameters as the consequent of fuzzy ' rules, the zero-order T-S fuzzy control based on niche is constructed. The controller is designed by using the method of Lyapunov's synthesis and the consequent parameters are determined by using a gradient descent method. Finally, the adaptive law of consequent parameters is gotten. The fuzzy system based on niche embodies biological individual's self-adaptivity, namely the individual is evolving in a direction that favors its development. So the proposed fuzzy T-S fuzzy system has better adaptivity, such as obtaining a smaller fluctuant tracking error, and the global stability of the closed-loop system can be assured. Simulation of two-dimensional predator system tracking is carried out to verify the design approach.

  9. Adaptive Fuzzy Systems in Computational Intelligence

    Science.gov (United States)

    Berenji, Hamid R.

    1996-01-01

    In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

  10. 柔性太阳能帆板振动变论域白适应模糊控制%Vibration Control of Flexible Solar Panel Based on Variable Universe Adaptive Fuzzy Control

    Institute of Scientific and Technical Information of China (English)

    许睿; 李东旭

    2012-01-01

    The fuzzy vibration control for the flexible solar panel with piezoelectric smart structure was studied in this paper. The dynamical equations of the solar panel were derived. Based on period variable universe, a variable universe adaptive fuzzy controller was designed according to the speciality of the vibration, which improved precision and adaptive ability of fuzzy control. The simulation results showed that the variable universe adaptive fuzzy controller could suppress the vibrations of the flexible spacecraft solar panel effectively, and it was better than the simple fuzzy controller.%研究了有压电智能结构的柔性太阳能帆板振动的模糊控制。建立了帆板的动力学方程,针对振动问题的特殊性,采用周期变论域设计了变论域自适应模糊控制器,提高了模糊控制的精度和自适应性能。仿真结果表明:变论域自适应模糊控制能有效抑制柔性太阳能帆板的振动,并明显优于简单模糊控制。

  11. Differential Evolution Algorithm for System Identification and Tuning of a Fuzzy Modified Model Reference Adaptive Controller for a Coupled Tank Level Process

    Directory of Open Access Journals (Sweden)

    K. Asan Mohideen

    2014-07-01

    Full Text Available Improving the transient performance of the MRAC has been a point of research for a long time. The main objective of the paper is to design an MRAC with improved transient and steady state performance. This paper proposes a Fuzzy modified MRAC (FMRAC to control a coupled tank level process. The FMRAC uses a proportional control based Mamdani-type Fuzzy inference system (MFIS to improve the transient performance of a direct MRAC. In addition, it proposes the application of Differential Evolution (DE algorithm to tune the membership function parameters off-line of the FMRAC to improve its performance further. The proposed controller is called DE based Fuzzy Modified Model Reference Adaptive Controller (DEFMRAC. In this study, an MRAC, an FMRAC and the proposed DEFMRAC are designed for a coupled tank level process and their performances are compared. The coupled tank level process is modeled by using system identification procedure and the accuracy of the resultant model is further improved by parameter tuning using DE. The simulation results show that the FMRAC gives better transient performance than the direct MRAC. The results also show that the proposed DEFMRAC gives better transient performance than the direct MRAC or the FMRAC. It is concluded that the proposed controller can be used to obtain very good transient and steady state performance in the control of nonlinear processes.

  12. Fuzzy Adaptive Tension Controller Based on Genetic Algorithm%优化GA的模糊自适应张力控制

    Institute of Scientific and Technical Information of China (English)

    靳文军; 刘伯鸿; 徐志奇; 刘丽媛

    2013-01-01

    以典型的卷绕张力系统为研究对象,结合模糊自适应PID控制与遗传算法,设计了基于遗传算法的模糊自适应PID张力控制器.该控制器由离线和在线两部分组成,离线部分利用遗传算法搜索出一组最优的PID参数作为在线调节的初始值,在线部分用来实时调整系统响应的PID参数.仿真结果表明,采用的模糊自适应PID控制策略较传统PID控制器具有响应速度快、控制输出稳定、抗干扰能力强、鲁棒性好等优点.%With typical winding tension system as researching object,by combining fuzzy adaptive PID control and generic algorithm,the fuzzy adaptive PID tension controller based on generic algorithm has been designed.The controller is composed of two parts,the off line one and the on line one.With generic algorithm,the off line part searches the optimal PID parameters for using as the initial regulation of the online part;the online part adjusts the PID parameters in real time to response the system.The result of simulation shows that the fuzzy adaptive PID control strategy features faster response speed,more stable control output,higher anti-interference capability and robustness than those of traditional PID controller.

  13. Modelling on fuzzy control systems

    Institute of Scientific and Technical Information of China (English)

    LI; Hongxing(李洪兴); WANG; Jiayin(王加银); MIAO; Zhihong(苗志宏)

    2002-01-01

    A kind of modelling method for fuzzy control systems is first proposed here, which is calledmodelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method thatis different from two well-known modelling methods, that is, the first modelling method, mechanism modellingmethod (MMM), and the second modelling method, system identification modelling method (SlMM). Thismethod can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inferencerules describing a practice system into a kind of nonlinear differential equation with variable coefficients, calledHX equations, so that the mathematical model of the system can be obtained. This means that we solve thedifficult problem of how to get a model represented as differential equations on a complicated or fuzzy controlsystem.

  14. 基于风能转换系统的模糊PID自适应控制%Wind Energy Conversion Systems Using Fuzzy Self-Adaptive PID Control

    Institute of Scientific and Technical Information of China (English)

    李意扬; 吴定会

    2013-01-01

    In order to capture maximum energy under rated wind speed, a fixed pitch wind power generation control system based on fuzzy self-adaptive PID technology was designed. A variable speed fixed pitch controller of the wind energy conversion system based on the fuzzy self-adaptive PID technology was proposed, then the wind turbine and the variable speed fixed pitch models were set up. By using the generator power error calculated from theoretical maximum captured wind power as input, the controllers parameters were regulated online according to the wind conditions, which was self-adaptive. The simulation results demonstrated that feasibility of the proposed fuzzy control method based on the gauss membership function. With the proposed controller the wind energy capture ratio could be maintained around its optimal value 0. 476 and the tip speed ratio around its optimal value 7. It was able to capture the maximum power below the rated wind velocity.%以额定风速以下风能的最大捕获为目标,设计了基于模糊PID自适应控制的桨距控制器.以发电机产生的电能与理论计算的风轮最大捕获的风能误差为输入设计控制器,该控制器在运行时根据风况在线调整PID参数,实现自整定.仿真结果表明,在额定风速以下,基于高斯型隶属度函数的模糊控制方法能够将风能转换系数控制在最优值0.476附近,叶尖速比可以维持在最优值7附近,能够实现额定风速以下的最大风能捕获.

  15. Design of Magnetic Levitation System Based on Inverse Control Techniqueusing Adaptive Neuro-Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Mithaq Nama Raheema

    2017-07-01

    Full Text Available The design of ANFIS network based inverse control technique is proposed in this paperfor this system. Simulation is implemented in MATLAB after the ANFIS is trained and it is shown that results are applicable in process industry and acceptable for reference control applications. The effectiveness of the proposed ANFIS in inverse controller it has been tested by entering random selected points which represent the values of input voltage from the system under control as a reference input to inverse modelling, after that entering the results of inverse modelling to the modelling of magnet levitation system to form the desired output. The result is acceptable with small errors about 0.0011

  16. Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

    Directory of Open Access Journals (Sweden)

    Y. A. Al-Turki

    2012-01-01

    Full Text Available This paper presents a powerful supervisory power system stabilizer (PSS using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS. The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC driven by a fixed fuzzy set (FFS which has 49 rules. Both fuzzy logic controller (FLC algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study.

  17. 基于Fuzzy Logic的PID自适应控制仿真%Simulation Research on PID Self Adaptive Control Based on Fuzzy Logic

    Institute of Scientific and Technical Information of China (English)

    刘文江; 马思根; 刘文海

    2009-01-01

    介绍了基于Fuzzy Logic的模糊控制原理,结合基于Fuzzy Logic的模糊控制和传统PID控制的优点,提出基于Fuzzy Logic的在线整定PID参数的自适应控制.Matlab仿真结果表明,控制系统具有很好的鲁棒性.

  18. Control of a methanol reformer system using an Adaptive Neuro‐Fuzzy Inference System approach

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andersen, John; Ehmsen, Mikkel Præstholm

    This work presents a stoichiometry control strategy for a reformed methanol fuel cell system, which uses a reformer to produce hydrogen for an HTPEM fuel cell. One such system is the Serenus H3-350 battery charger developed by the Danish company Serenegy® which this work is based on. The poster...

  19. Methanol Reformer System Modeling and Control using an Adaptive Neuro-Fuzzy Inference System approach

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Ehmsen, Mikkel Præstholm; Andersen, John

    2012-01-01

    This work presents the experimental study and modelling of a methanol reformer system for a high temperature polymer electrolyte membrane (HTPEM) fuel cell stack. The analyzed system is a fully integrated HTPEM fuel cell system with a DC/DC control output able to be used as e.g. a mobile battery...... charger. The advantages of using a HTPEM methanol reformer is that the high quality waste heat can be used as a system heat input to heat and evaporate the input methanol/water mixture which afterwards is catalytically converted into a hydrogen rich gas usable in the high CO tolerant HTPEM fuel cells....... Creating a fuel cell system able to use a well known and easily distributable liquid fuel such as methanol is a good choice in some applications such as range extenders for electric vehicles as an alternative to compressed hydrogen. This work presents a control strategy called Current Correction...

  20. Simulation Study on Fuzzy Control of Rotary Steering Drilling Trajectory

    Directory of Open Access Journals (Sweden)

    Xue Qi-Long

    2012-07-01

    Full Text Available The purpose of this study is to establish a control method to make borehole trajectory smoother. Considering that the complexity of rotary steerable drilling trajectory control and uncertainty of underground work, analysis of the deficiencies for the traditional trajectory control and the rotary steerable drilling trajectory deviation vector control theory, introduced the concept of "trend Angle", combined with the deviation vector as joint control variables, using fuzzy control algorithm that established of rotary steerable drilling trajectory fuzzy control model. Designed the fuzzy controller using Matlab/Simulink toolbox and dynamic simulation analysis for the fuzzy control systems, simulation results show that the designed fuzzy controller can effectively track the well path design, has a strong adaptability and control results is better than traditional PID control method.

  1. Urban Intersection Traffic Signal Control Based on Fuzzy Logic

    Institute of Scientific and Technical Information of China (English)

    魏武; 张毅; 张佐; 宋靖雁

    2002-01-01

    This paper presents a fuzzy logic adaptive traffic signal control method for an isolated four-approach intersection with through and left-turning movements. In the proposed method, the fuzzy logic controller can make adjustments to signal timing in response to observed changes. The "urgency degree" term that can describe different user's demands for a green light is used in the fuzzy logic decision-making. In addition, a three-level fuzzy controller model decides whether to extend or terminate the current signal phase and the sequence of phases. Simulation results show that the fuzzy controller can adjust its signal timing in response to changing traffic conditions on a real-time basis and that the proposed fuzzy logic controller leads to less vehicle delays and a lower percentage of stopped vehicles.

  2. How to combine probabilistic and fuzzy uncertainties in fuzzy control

    Science.gov (United States)

    Nguyen, Hung T.; Kreinovich, Vladik YA.; Lea, Robert

    1991-01-01

    Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. In many cases, in addition to the experts' rules, additional statistical information about the system is known. It is explained how to use this additional information in fuzzy control methodology.

  3. 一类时滞系统的自校正模糊Smith控制器设计%Adaptive-Fuzzy-Smith Controller Design of a Class of Systems with Time Delay

    Institute of Scientific and Technical Information of China (English)

    徐洪洲; 龚磊

    2009-01-01

    Smith预估控制是解决时滞系统控制的有效方法,但其对系统模型参数的精确要求限制了其应用.而模糊控制本质上属于PD控制,无法消除系统的稳态误差.文中设计了一种自校正模糊Smith 控制器,结合模糊控制、Smith预估器及自校正控制的优点,既保持了模糊控制鲁棒性较强的特点,又消除了系统的稳态误差,且改善了系统的动态性能.仿真结果表明了自校正模糊控制器的有效性和优越性.%Smith predictive controller is an effective method to control the time delay systems, however, the drawback of high rigourness of Smith controller limits its application. Fuzzy-Smith control belongs to PD control and can't avoid the steady-state error. An adaptive-Fuzzy-Smith controller is designed, combining the advantages of fuzzy control, Smith predictive control and adaptive control, it keeps the good robustness of fuzzy control and improves the dynamic performance of the system. The simulation results show the effectiveness and superiority of the adaptive-Fuzzy-Smith controller.

  4. Fuzzy logic control of telerobot manipulators

    Science.gov (United States)

    Franke, Ernest A.; Nedungadi, Ashok

    1992-01-01

    Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems.

  5. An Order Release Control Mechanism Based on self-Adaptive Neural Fuzzy Inference System and Theory of Constraints

    Directory of Open Access Journals (Sweden)

    Chuandong Zhan

    2013-11-01

    Full Text Available Order release is the key premise for the semiconductor wafer fabrication system to perform well, which is also one of the paramount significant components in the scheduling strategies. Most order release strategies merely have focused on the workloadbut failed in considering the remarkable influence oncycletime of common orders that is brought by unexpectedrushones.In this paper an on-linemechanismbased on Theory of Constraintsfor lot releaseusingself-Adaptive Neural Fuzzy Inference System modelswas presentedwhich is able to adjust the release rhythmdynamicallyaccording to dynamics of fabs.In our approach, an ANFIS model was established to predict the ratiobetweenhotand common lotsin wafer fabto perform adjustments on the order release schedule in advance.Simulated experimentsbased on the HP24 model were carefully performed and experimental results proved a better performance of common lotsthan original TOC on a large scale, especially when it comes to the situation of disturbance.  

  6. 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.

  7. Linear Design Approach to a Fuzzy Controller

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1999-01-01

    A ball-balancer, basically an inverted pendulum problem, is stabilised by a linear controller. With certain design choices, a fuzzy controller is equivalent to a summation; thus it can replace the linear controller. It can be claimed, that the fuzzy controller performs at least as well...... as the linear controller, since the linear controller is contained in the fuzzy controller. The approach makes it somewhat easier to design a fuzzy controller....

  8. A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

    Directory of Open Access Journals (Sweden)

    Camilo Caraveo

    2017-07-01

    Full Text Available Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.

  9. 基于PLC的锅炉液位模糊自适应PID控制%Fuzzy Self-Adaptive PID Control of Boiler Liquid Level Based on PLC

    Institute of Scientific and Technical Information of China (English)

    郑文; 张运波

    2013-01-01

    以小型PLC锅炉液位控制为例,介绍了PLC实现锅炉液位模糊自适应PID控制的方法,并总结了PLC实现模糊自适应PID程序设计的关键技术。实验结果表明,无论是动态指标,还是静态指标,都比常规PID控制优越,为应用PLC实现复杂控制算法提供了程序设计方法。%Take the boiler’s liquid level control based on small PLC as an example, the realization method of boiler’s liquid level fuzzy self-adaptive PID control is introduced and the key technology of fuzzy self-adaptive PID program design by PLC is summarized. Through the experimental results, dynamic index and static index are superior to the normal PID. The method of program design is provided to realize the complex control algorithm based-on PLC.

  10. A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems

    Institute of Scientific and Technical Information of China (English)

    王攀; 徐承志; 冯珊; 徐爱华

    2002-01-01

    This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.

  11. A New Fuzzy Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FANG Lei; ZHANG Huan-chun; JING Ya-zhi

    2005-01-01

    Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution.A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.

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

    Directory of Open Access Journals (Sweden)

    Asan Mohideen Khansadurai

    2014-01-01

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

  13. Adaptive fuzzy control method for EMG prosthetic hand%一种肌电假手的自适应模糊控制方法

    Institute of Scientific and Technical Information of China (English)

    吴常铖; 宋爱国; 章华涛

    2013-01-01

    针对肌电假手的力控制问题,提出了一种基于肌电信号自适应学习的动作识别方法,同时结合模糊神经网络PID控制算法实现肌电信号对假手的控制.设计的肌电信号自适应学习动作识别方法在时域内进行,减小运算复杂度和计算量的同时保证了动作识别精度.采用模糊神经网络PID算法设计了假手握力控制器,在没有位置传感器的情况下保证了假手握力的控制精度.进行了肌电信号动作识别跟踪实验、假手握力跟踪实验、肌电信号控制假手抓取实验,实验结果证明了肌电信号自适应学习动作识别方法和模糊神经网络PID握力控制方法在肌电假手控制中的有效性.%Aiming at the force control issue of EMG prosthetic hand,an action identification method based on adaptive learning of EMG signal is proposed and combined with fuzzy neural network PID control algorithm,which implements the prosthetic hand control with EMG signal.The designed action identification method based on adaptive learning of EMG signal is performed in time domain,which ensures the action identification accuracy,while reducing the computational complexity and computational task.A prosthetic hand grip force controller is designed with fuzzy neural network PID algorithm,which ensures the grip force control accuracy without using a position sensor.The EMG signal action identification and tracking experiment,prosthetic hand grip force tracking experiment and EMG signal controlled prosthetic hand grip experiment were carried out.The experiment results prove the effectiveness of the designed EMG signal adaptive learning action identification method and fuzzy neural network PID grip force control method in EMG prosthetic hand control.

  14. A Novel Adaptive Fuzzy Controller for Application in Autonomous Vehicles%一种应用于自主车辆的新型自适应模糊控制器

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Adqptive control for a class of nonlinear systems is discussed in thes paper ,We use fuzzy systems to approximate the ideal optimal controller by aduusting the parameters of fuzzy systems,In order to tune these parameters,linear relationship between aproximation error and parameters in established firse. Then we design the adaptive laws of these parameters based on Lysapunov synthesis approach.The advantage of our method is that we can tune not only the parameters of the consequences of fuzzy rules,but also the parameters of the membership functions.As a result ,a stable and nore flexible controller is achieved.Hye performance of the adaptive scheme is demonstrated through the longitudinal vehicle control.

  15. Learning fuzzy logic control system

    Science.gov (United States)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  16. Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm

    OpenAIRE

    Afrakoti, Iman Esmaili Paeen; Shouraki, Saeed Bagheri; Merrikhbayat, Farnood

    2013-01-01

    Although fuzzy techniques promise fast meanwhile accurate modeling and control abilities for complicated systems, different difficulties have been re-vealed in real situation implementations. Usually there is no escape of it-erative optimization based on crisp domain algorithms. Recently memristor structures appeared promising to implement neural network structures and fuzzy algorithms. In this paper a novel adaptive real-time fuzzy modeling algorithm is proposed which uses active learning me...

  17. Fuzzy Control for Food Agricultural Robotics of a Degree

    Directory of Open Access Journals (Sweden)

    Lepeng Song

    2014-02-01

    Full Text Available In this study, we have a research of the fuzzy control for food agricultural robotics of a degree. Weeding robots can replace humans weeding activities, since the control system with nonlinear, robustness and a series of complex time-varying characteristics of the traditional PID control of the food agricultural robot end of the operation control effect cannot achieve the desired results, therefore, the design for the traditional use of classical PID control algorithm to control the food agricultural robot end of the operation of a series of drawbacks, combining cutting-edge control theory, fuzzy rule-based adaptive PID control strategy to control the entire system, so as to achieve the desired control effect. Experimental results show that the fuzzy adaptive PID control method for robot end postural control has better adaptability and track-ability.

  18. Adaptive backstepping fuzzy control for servo systems with backlash%考虑齿隙伺服系统的反步自适应模糊控制

    Institute of Scientific and Technical Information of China (English)

    杜仁慧; 吴益飞; 陈威; 陈庆伟

    2013-01-01

    An approximate dead-zone function is introduced to build the model of the electromechanical servo system with unknown parameters and nonlinear backlash; the method for selecting parameters of the approximate dead-zone function is also given. Two adaptive fuzzy logic systems are employed to approximate unknown parameters and the nonlinear part of the servo system online, to avoid the derivation of adaptive law for each unknown parameter. Adaptive fuzzy controller is also developed based on backstepping method, which effectively inhibits the influence of parameter uncertainties and backlash nonlinearity. It is theoretically shown by using Lyapunov function that the position tracking error converges exponentially. Finally simulations show that the adaptive backstepping fuzzy controller not only reduces gear transmitting torque oscillation significantly, but also have higher accuracy and robustness in performances than PID controller.%针对具有未知参数和齿隙非线性的机电伺服系统,引入一种近似死区函数建立了系统的数学模型,给出了死区函数中参数的选取方法.用两个自适应模糊逻辑系统在线逼近机电伺服系统中的未知参数和非线性环节,从而避免了对每个未知参数推导自适应律.基于反步法设计了自适应模糊控制器,可抑制未知参数和齿隙非线性对系统性能的影响.采用Lyapunov方法证明了位置跟踪误差的指数收敛性.与PID控制方法对比的仿真实验表明,本文方法能够显著减小齿轮间传递力矩的振荡,并具有很好的控制精度和鲁棒性.

  19. Fuzzy Based composition Control of Distillation Column

    Directory of Open Access Journals (Sweden)

    Guru.R

    2013-04-01

    Full Text Available This paper proposed a control scheme based on fuzzy logic for a methanol - water system of bubble cap distillation column. Fuzzy rule base and Inference System of fuzzy (FIS is planned to regulatethe reflux ratio (manipulated variable to obtain the preferred product composition (methanol for a distillation column. Comparisons are made with conventional controller and the results confirmed the potentials of the proposed strategy of fuzzy control.

  20. New Asymmetric Fuzzy PID Control for Pneumatic Position Control System

    Institute of Scientific and Technical Information of China (English)

    薛阳; 彭光正; 范萌; 伍清河

    2004-01-01

    A fuzzy control algorithm of asymmetric fuzzy strategy is introduced for a servo-pneumatic position system. It can effectively solve the difficult problems of single rod low friction cylinders, which are mainly caused by asymmetric structures and different friction characteristics in two directions. On the basis of this algorithm, a traditional PID control is used to improve dynamic performance. Furthermore, a new asymmetric fuzzy PID control with α factor is advanced to improve the self-adaptability and robustness of the system. Both the theoretical analyses and experimental results prove that, with this control strategy, the dynamic performance of the system can be greatly improved. The system using this control algorithm has strong robustness and it obtains desired overshoot and repeatability in both transient and steady-state responses.

  1. Mercer Kernel Based Fuzzy Clustering Self-Adaptive Algorithm

    Institute of Scientific and Technical Information of China (English)

    李侃; 刘玉树

    2004-01-01

    A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.

  2. 混合隔振系统自适应模糊滑模控制%Adaptive Fuzzy Sliding-mode Controller for Hybrid Vibration Isolation Systems

    Institute of Scientific and Technical Information of China (English)

    杨理华; 朱石坚; 楼京俊; 李棒

    2014-01-01

    针对机械设备被动隔振在低频段隔振效果较差的问题,建立磁致伸缩作动器的电—磁—机转化数学模型,提出一种基于自适应模糊滑模控制算法,并用李雅普诺夫方法证明控制器的稳定性,将该控制策略与磁致伸缩作动器应用于混合隔振系统中。仿真结果表明:在单频、多频及随即激励条件下,自适应模糊滑模控制器具有良好的动态特性和鲁棒性,能够提高系统隔振效率并拓宽隔振频段,有效减小传至基础的力。%Aiming at the problem of poor vibration isolation effect of passive vibration isolators of mechanical equip-ment in low frequency range, an electric-magnetic-mechanical conversion model for magnetostrictive actuators is estab-lished, and an adaptive fuzzy sliding-mode control algorithm is proposed. The stability of the controller is proved by Lyapu-nov method. Then, the control strategy and the magnetostrictive actuator are used in a hybrid vibration isolation system. The simulation results show that in whatever conditions of single frequency excitation, multi-frequency excitation or random ex-citation, the adaptive fuzzy sliding-mode controller has good dynamic characteristics and robustness. This property can also be used to improve the isolation efficiency and broaden the vibration isolation frequency band of the hybrid system, and ef-fectively reduce the force transmitted to the foundation of the mechanical equipment.

  3. 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.

  4. Fuzzy logic controllers on chip

    OpenAIRE

    Acosta, Nelson; Simonelli, Daniel Horacio

    2002-01-01

    This paper analyzes a fuzzy logic (FL) oriented instruction set (micro)controller and their implementations on FIPSOC1. VHDL code is synthesized using a small portion of FIPSOC FPGA2. This circuits are used from the mP8051 FIPSOC built-in microcontroller to provide efficient arithmetic operations such as multipliers, dividers, minimums and maximums.

  5. Students Classification With Adaptive Neuro Fuzzy

    Directory of Open Access Journals (Sweden)

    Mohammad Saber Iraji

    2012-07-01

    Full Text Available Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis has less error and can be used as an effective alternative system for classifying students

  6. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning for pitch control system

    Science.gov (United States)

    Li, Yezi; Xiao, Cheng; Sun, Jinhao

    2013-03-01

    PID and fuzzy PID controller are applied into the pitch control system. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist.

  7. Fuzzy Logic Controller Scheme for Floor Vibration Control

    Directory of Open Access Journals (Sweden)

    Nyawako Donald Steve

    2015-01-01

    Full Text Available The design of civil engineering floors is increasingly being governed by their vibration serviceability performance. This trend is the result of advancements in design technologies offering designers greater flexibilities in realising more lightweight, longer span and more open-plan layouts. These floors are prone to excitation from human activities. The present research work looks at analytical studies of active vibration control on a case study floor prototype that has been specifically designed to be representative of a real office floor structure. Specifically, it looks at tuning fuzzy control gains with the aim of adapting them to measured structural responses under human excitation. Vibration mitigation performances are compared with those of a general velocity feedback controller, and these are found to be identical in these sets of studies. It is also found that slightly less control force is required for the fuzzy controller scheme at moderate to low response levels and as a result of the adaptive gain, at very low responses the control force is close to zero, which is a desirable control feature. There is also saturation in the peak gain with the fuzzy controller scheme, with this gain tending towards the optimal feedback gain of the direct velocity feedback (DVF at high response levels for this fuzzy design.

  8. GA-Based Fuzzy Sliding Mode Controller for Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    W. L. Chiang

    2008-11-01

    Full Text Available Generally, the greatest difficulty encountered when designing a fuzzy sliding mode controller (FSMC or an adaptive fuzzy sliding mode controller (AFSMC capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. In this paper, we describe a method of stability analysis for a GA-based reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. First, we approximate and describe an uncertain and nonlinear plant for the tracking of a reference trajectory via a fuzzy model incorporating fuzzy logic control rules. Next, the initial values of the consequent parameter vector are decided via a genetic algorithm. After this, an adaptive fuzzy sliding model controller, designed to simultaneously stabilize and control the system, is derived. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov's direct method. Finally, an example, a numerical simulation, is provided to demonstrate the control methodology.

  9. 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.

  10. Tuning of a neuro-fuzzy controller by genetic algorithm.

    Science.gov (United States)

    Seng, T L; Bin Khalid, M; Yusof, R

    1999-01-01

    Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.

  11. Fuzzy controllers based on some fuzzy implication operators and their response functions

    Institute of Scientific and Technical Information of China (English)

    LI Hongxing; YOU Fei; PENG Jiayin

    2004-01-01

    The fuzzy controllers constructed by 23 fuzzy implication operators based on CRI algorithm and their response functions are discussed.The conclusions show that the fuzzy controllers constructed by 9 fuzzy implication operators are universal approximators to continuous functions and can be used in practical fuzzy control systems.And these 9 fuzzy implication operators except for Einstein operator intersection are all the adjoint pairs of some fuzzy implication operators.Besides, there are 3 other fuzzy controllers formed by fuzzy implication operators being regarded approximately as fitted functions.

  12. Fuzzy logic based robotic controller

    Science.gov (United States)

    Attia, F.; Upadhyaya, M.

    1994-01-01

    Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.

  13. 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)

  14. 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.

  15. Adaptive Neuro-fuzzy approach in friction identification

    Science.gov (United States)

    Zaiyad Muda @ Ismail, Muhammad

    2016-05-01

    Friction is known to affect the performance of motion control system, especially in terms of its accuracy. Therefore, a number of techniques or methods have been explored and implemented to alleviate the effects of friction. In this project, the Artificial Intelligent (AI) approach is used to model the friction which will be then used to compensate the friction. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is chosen among several other AI methods because of its reliability and capabilities of solving complex computation. ANFIS is a hybrid AI-paradigm that combines the best features of neural network and fuzzy logic. This AI method (ANFIS) is effective for nonlinear system identification and compensation and thus, being used in this project.

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

    Directory of Open Access Journals (Sweden)

    ESMAIL SADEGHINASAB

    2016-11-01

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

  17. 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.

  18. Fuzzy Controlled Parallel PSO to Solving Large Practical Economic Dispatch

    OpenAIRE

    Mahdad, Belkacem; Srairi, Kamel; BOUKTIR, Tarek; Benbouzid, Mohamed

    2010-01-01

    International audience; This paper proposes a version of fuzzy controlled parallel particle swarm optimization approach based decomposed network (FCP-PSO) to solve large nonconvex economic dispatch problems. The proposed approach combines practical experience extracted from global database formulated in fuzzy rules to adjust dynamically the three parameters associated to PSO mechanism search. The adaptive PSO executed in parallel based in decomposed network procedure as a local search to expl...

  19. Fuzzy neural networks for arc welding quality control

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Fuzzy Logic Control (FLC) is a promising control strategy in welding process control due to its ability for solving control problem with uncertainty as well as its independence on the analytical mathematics model. However, in basic FLC, the fuzzy rule relies heavily on the experts' (e.g. advanced welders') experience. In addition to this, the membership function for fuzzy set is non-adaptive, i.e. it remains unchanged as long as they are determined by experience or other means. For welding process, which is time-variable systems and strong disturbance exists in it, fixed membership function may not guarantee the required system performance, and attempts should be made to improve the system performance by adopting adaptive membership function. Therefore, the automatically determination of the fuzzy rule and in-process adaptation of membership function are required for the advanced welding process control. This paper discussed the possibility by using the combination between FLC and neural network (NN) to realize the above propose. The adaptation of membership function as well as the self-organizing of fuzzy rule are realized by the self-learning and competitiveness of the NN. Taking GTAW process welds bead width regulating system as the controlled plant, the proposed algorithm was testified for such a process. Computer simulations showed the improvement of the system characteristics.

  20. 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...

  1. CRUISE FUZZY CONTROL FOR AUTOMOBILE WITH CVT

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    To develop cruise control system of an automobile with the metal pushing V-belt type CVT, the dynamic model of automobile travelling longitudinally is established, and the fuzzy controller of control system is designed. Considering uncertainty system parameter and exterior resistance disturbances, the stability of controller is investigated by simulating. The results of its simulation show that the fuzzy controller designed has practicability.

  2. A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper,an adaptive dynamic control scheme based on a fuzzy neural network is presented,that presents utilizes both feed-forward and feedback controller elements.The former of the two elements comprises a neural network with both identification and control role,and the latter is a fuzzy neural algorithm,which is introduced to provide additional control enhancement.The feedforward controller provides only coarse control,whereas the feedback oontroller can generate on-line conditional proposition rule automatically to improve the overall control action.These properties make the design very versatile and applicable to a range of industrial applications.

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

    Directory of Open Access Journals (Sweden)

    Luis Daniel Lledó

    2015-03-01

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

  4. Analysis of inventory difference using fuzzy controllers

    Energy Technology Data Exchange (ETDEWEB)

    Zardecki, A.

    1994-08-01

    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.

  5. 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.

  6. Advanced Control Techniques with Fuzzy Logic

    Science.gov (United States)

    2014-06-01

    AFRL-RQ-WP-TR-2014-0175 ADVANCED CONTROL TECHNIQUES WITH FUZZY LOGIC James E. Combs Structural Validation Branch Aerospace Vehicles...TECHNIQUES WITH FUZZY LOGIC 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62201F 6. AUTHOR(S) James E. Combs...unlimited. 13. SUPPLEMENTARY NOTES PA Case Number: 88ABW-2014-3281; Clearance Date: 09 Jul 2014. 14. ABSTRACT Research on the Fuzzy Logic control

  7. 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.

  8. The Self-Organising Fuzzy Controller

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    A marginally stable test system, with a large dead time and an integrator, is stabilised by a self-organising fuzzy controller in a simulation study. It acts as a case study, to explain the self-organising controller to engineering students. The paper is one of a series of tutorial papers...... for a course in fuzzy control....

  9. The Self-Organising Fuzzy Controller

    DEFF Research Database (Denmark)

    Jantzen, Jan

    1998-01-01

    A marginally stable test system, with a large dead time and an integrator, is stabilised by a self-organising fuzzy controller in a simulation study. It acts as a case study, to explain the self-organising controller to engineering students. The paper is one of a series of tutorial papers...... for a course in fuzzy control....

  10. Fuzzy Adaptive Control of Stochastic Nonlinear Systems with Unknown Virtual Control Gain Function%虚拟控制增益函数未知的随机非线性系统模糊自适应控制

    Institute of Scientific and Technical Information of China (English)

    王迎春; 张化光; 王以忠

    2006-01-01

    The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is extended to some extent, and accordingly, the results of global ultimate boundedness for stochastic nonlinear systems are developed. Next, a new design scheme of fuzzy adaptive control is proposed. The advantage of it is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time,the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.

  11. Application of Improved Fuzzy Controller in Networked Control System

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qian; GUO Xi-jin; WANG Zhen; TIAN Xi-lan

    2006-01-01

    Aiming at the problem of network-induced delay and data dropout in networked control system, an improved fuzzy controller is proposed in this paper. Considering the great influence of a controller on the performance of control system, an improved controller with a second order fuzzy controller and network-induced delay compensator being added to the basic fuzzy controller is proposed to realize self-regulation on-line. For this type of controller, neither plant model nor measurement of network delay is required. So it is capable of automatically adjusting quantified factor, proportional factor, and integral factor according to the control system error and its derivative. The design makes full use of the advantages of quickness in operation and reduction of steady state error because of its integral function. The controller has a good control effect on time-delay and can keep a better performance by self-regulation on-line in the network with data dropout and interference. It is good in quickness, adaptability, and robustness, which is favorable for controlling the long time-delay system.

  12. Refining fuzzy logic controllers with machine learning

    Science.gov (United States)

    Berenji, Hamid R.

    1994-01-01

    In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

  13. A fuzzy control design case: The fuzzy PLL

    Science.gov (United States)

    Teodorescu, H. N.; Bogdan, I.

    1992-01-01

    The aim of this paper is to present a typical fuzzy control design case. The analyzed controlled systems are the phase-locked loops (PLL's)--classic systems realized in both analogic and digital technology. The crisp PLL devices are well known.

  14. Control Difuso Adaptivo y su Aplicación a una Planta Piloto de Secado por Lecho Fluidizado Adaptive Fuzzy Control with an Application to a Fluidized Dryer Bed Pilot Plant

    Directory of Open Access Journals (Sweden)

    I. Velásquez

    2005-01-01

    Full Text Available En el presente trabajo, se muestra el diseño e implementación de un controlador difuso proporcional-prealimentado con Mecanismo de Adaptación (CDPP-MA para regular la temperatura en el interior del de un secador por lecho fluidizado. El algoritmo del controlador difuso se programa en el “Script” de Intouch, mientras que para diseñar y configurar los diferentes parámetros del controlador difuso se utiliza el software Matlab. Las pruebas se llevaron a cabo en una Planta Piloto e incluyeron cambios en la referencia del controlador y la aplicación de perturbaciones al sistema. El controlador presenta buen desempeño, tanto en el seguimiento de la referencia como en la atenuación de las perturbaciones. Los resultados indican que el controlador implementado es una alternativa factible de ser usado en el control de procesos no lineales o fuertemente perturbados.This work presents the design and implementation of a feed-forward proportional fuzzy controller with an adaptation mechanism (CDPP-MA. This controller was designed for temperature feedback control in a fluidized bed reactor. The fuzzy controller algorithm is programmed in Intouch’s Script language. Matlab software was used for the design and configuration of the fuzzy controller’s parameters. Experimental tests were carried out in a pilot plant and included changes of reference of the controller and applications of disturbances to the system. Excellent performance was obtained with the controller, both in following the reference and in attenuation of the disturbances. The results show the implemented controller to be a feasible alternative to for use in control of highly non-linear or strongly disturbed processes

  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 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.

  17. Adaptive Critic Based Neuro-Fuzzy Tracker for Improving Conversion Efficiency in PV Solar Cells

    Directory of Open Access Journals (Sweden)

    Halimeh Rashidi

    2012-08-01

    Full Text Available The output power of photovoltaic systems is directly related to the amount of solar energy collected by the system and it is therefore necessary to track the sun’s position with high accuracy. This study proposes multi-agent adaptive critic based nero fuzzy solar tracking system dedicated to PV panels. The proposed tracker ensures the optimal conversion of solar energy into electricity by properly adjusting the PV panels according to the position of the sun. To evaluate the usefulness of the proposed method, some computer simulations are performed and compared with fuzzy PD controller. Obtained results show the proposed control strategy is very robust, flexible and could be used to get the desired performance levels. The response time is also very fast. Simulation results that have been compared with fuzzy PD controller show that our method has the better control performance than fuzzy PD controller.

  18. 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.

  19. Improvement on fuzzy controller design techniques

    Science.gov (United States)

    Wang, Paul P.

    1993-01-01

    This paper addresses three main issues, which are somewhat interrelated. The first issue deals with the classification or types of fuzzy controllers. Careful examination of the fuzzy controllers designed by various engineers reveals distinctive classes of fuzzy controllers. Classification is believed to be helpful from different perspectives. The second issue deals with the design according to specifications, experiments related to the tuning of fuzzy controllers, according to the specification, will be discussed. General design procedure, hopefully, can be outlined in order to ease the burden of a design engineer. The third issue deals with the simplicity and limitation of the rule-based IF-THEN logical statements. The methodology of fuzzy-constraint network is proposed here as an alternative to the design practice at present. It is our belief that predicate calculus and the first order logic possess much more expressive power.

  20. Integrated fuzzy logic and genetic algorithms for multi-objective control of structures using MR dampers

    Science.gov (United States)

    Yan, Gang; Zhou, Lily L.

    2006-09-01

    This study presents a design strategy based on genetic algorithms (GA) for semi-active fuzzy control of structures that have magnetorheological (MR) dampers installed to prevent damage from severe dynamic loads such as earthquakes. The control objective is to minimize both the maximum displacement and acceleration responses of the structure. Interactive relationships between structural responses and input voltages of MR dampers are established by using a fuzzy controller. GA is employed as an adaptive method for design of the fuzzy controller, which is here known as a genetic adaptive fuzzy (GAF) controller. The multi-objectives are first converted to a fitness function that is used in standard genetic operations, i.e. selection, crossover, and mutation. The proposed approach generates an effective and reliable fuzzy logic control system by powerful searching and self-learning adaptive capabilities of GA. Numerical simulations for single and multiple damper cases are given to show the effectiveness and efficiency of the proposed intelligent control strategy.

  1. Temperature Control System Using Fuzzy Logic Technique

    Directory of Open Access Journals (Sweden)

    Isizoh A N

    2012-06-01

    Full Text Available Fuzzy logic technique is an innovative technology used in designing solutions for multi-parameter and non-linear control models for the definition of a control strategy. As a result, it delivers solutions faster than the conventional control design techniques. This paper thus presents a fuzzy logic based-temperature control system, which consists of a microcontroller, temperature sensor, and operational amplifier, Analogue to Digital Converter, display interface circuit and output interface circuit. It contains a design approach that uses fuzzy logic technique to achieve a controlled temperature output function.

  2. 非线性不确定系统的自适应模糊跟踪控制%Adaptive Fuzzy Tracking Control of a Class of Nonlinear Uncertain Systems

    Institute of Scientific and Technical Information of China (English)

    张月; 薛红; 陈兵

    2011-01-01

    This paper is concerned with the problem of adaptive fuzzy tracking for a class of nonlinear systems with the uncertainties. Fuzzy logic systems are used to approximate the unknown nonlinear functions, then the adaptive fuzzy tracking controller is designed by using the backstepping technique and Lyapunov functionals. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. An advantage of the proposed control scheme lies in that the design of fuzzy controller does not involve the fuzzy bases vector function. Therefore the proposed controller,when used to control system, will greatly reduce the system online calculation burden. Finally, one example is used to demonstrate the effectiveness of our results proposed in this paper.%针对一类带有未知非线性函数的非线性不确定系统,提出了一种新的自适应模糊跟踪控制方案.模糊逻辑系统用于系统中的未知的非线性函数建模,然后基于backstep-ping方法和自适应技术设计模糊自适应控制器.所设计的模糊自适应控制器确保闭环系统的所有信号是一致有界的,同时跟踪误差收敛到原点的一个充分小的邻域内.另外所设计的控制器不涉及模糊基向量函数,因此所提出的控制器用于控制系统时,将极大地降低系统的在线计算负担.仿真算例验证了所提出方法的有效性.

  3. Smart Spectrometer for Distributed Fuzzy Control

    CERN Document Server

    Benoit, Eric

    2009-01-01

    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 functionalities like signal processing, or configuration, only few of them include functionalities to elaborate the fuzzy representation of measurements. In this paper, we develop a solution where the numeric processing is performed locally by the sensor, and where fuzzy processing is exported towards another computing resource by means of the CAN network. This paper presents the concept and the application to a smart fuzzy spectrometer.

  4. Parallel Fuzzy P+Fuzzy I+Fuzzy D Controller:Design and Performance Evaluation

    Institute of Scientific and Technical Information of China (English)

    Vineet Kumar; A.P.Mittal

    2010-01-01

    In this paper,a parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative (FP+FI+FD) controller is proposed.It is derived from the conventional parallel proportional-integral-derivative (PID) controller.It preserves the linear structure of a conventional parallel PID controller,with analytical formulas.The final shape of the controller is a discrete-time fuzzy version of a conventional parallel PID controller.Computer simulations are performed to evaluate the performance of the FP+FI+FD controller for setpoint tracking and load-disturbance rejection for some complex processes,such as first-and second-order processes with delay,inverse response process with and without delay and higher order processes.Also,the performance of the proposed fuzzy controller is evaluated experimentally on highly nonlinear liquid-flow process with a hysteresis characteristic due to a pneumatic control valve.The simulation and real time control is done using National InstrumentTM hardware and software (LabVIEWTM).The response of the FP+FI+FD controller is compared with the conventional parallel PID controller,tuned with the Ziegler-Nichols (Z-H) and (A)str(o)mH(a)gglund (A-H) tuning technique.It is observed that the FP+FI+FD controller performed much better than the conventional PI/PID controller.Simulation and experimental results demonstrate the effectiveness of the proposed parallel FP+FI+FD controller.

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

    African Journals Online (AJOL)

    2010-12-31

    Dec 31, 2010 ... motor (DFIM) with a fuzzy sliding mode controller (FSMC). ... becoming a major candidate in high-performance motion control applications, where ..... residual vibrations in high frequencies [17] (chattering phenomenon).

  6. control of a dc motor using fuzzy logic control algorithm

    African Journals Online (AJOL)

    user

    conditions such as changes in motor load demand, non- linearity ... Figure 1: Structure of a fuzzy logic controller (Source. [6]). A typical fuzzy logic ... mathematical modeling based on first principles; and via ..... applied. On the premise of these findings, it would be tactful in ... and Sugeno Type Fuzzy Inference Systems for Air.

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

    Science.gov (United States)

    Yasin, Saad Yaser

    2002-09-01

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

  8. Development of quantum-based adaptive neuro-fuzzy networks.

    Science.gov (United States)

    Kim, Sung-Suk; Kwak, Keun-Chang

    2010-02-01

    In this study, we are concerned with a method for constructing quantum-based adaptive neuro-fuzzy networks (QANFNs) with a Takagi-Sugeno-Kang (TSK) fuzzy type based on the fuzzy granulation from a given input-output data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive quantum clustering. This clustering technique is not only an extension of ideas inherent to scale-space and support-vector clustering but also represents an effective prototype that exhibits certain characteristics of the target system to be modeled from the fuzzy subtractive method. Furthermore, we developed linear-regression QANFN (LR-QANFN) as an incremental model to deal with localized nonlinearities of the system, so that all modeling discrepancies can be compensated. After adopting the construction of the linear regression as the first global model, we refined it through a series of local fuzzy if-then rules in order to capture the remaining localized characteristics. The experimental results revealed that the proposed QANFN and LR-QANFN yielded a better performance in comparison with radial basis function networks and the linguistic model obtained in previous literature for an automobile mile-per-gallon prediction, Boston Housing data, and a coagulant dosing process in a water purification plant.

  9. Fuzzy Sliding Mode Control of Plate Vibrations

    Directory of Open Access Journals (Sweden)

    Manu Sharma

    2010-01-01

    Full Text Available In this paper, fuzzy logic is meshed with sliding mode control, in order to control vibrations of a cantilevered plate. Test plate is instrumented with a piezoelectric sensor patch and a piezoelectric actuator patch. Finite element method is used to obtain mathematical model of the test plate. A design approach of a sliding mode controller for linear systems with mismatched time-varying uncertainties is used in this paper. It is found that chattering around the sliding surface in the sliding mode control can be checked by the proposed fuzzy sliding mode control approach. With presented fuzzy sliding mode approach the actuator voltage time response has a smooth decay. This is important because an abrupt decay can excite higher modes in the structure. Fuzzy rule base consisting of nine rules, is generated from the sliding mode inequality. Experimental implementation of the control approach verify the theoretical findings. For experimental implementation, size of the problem is reduced using modal truncation technique. Modal displacements as well as velocities of first two modes are observed using real-time kalman observer. Real time implementation of fuzzy logic based control has always been a challenge because a given set of rules has to be executed in every sampling interval. Results in this paper establish feasibility of experimental implementation of presented fuzzy logic based controller for active vibration control.

  10. ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers

    Science.gov (United States)

    César, Manuel Braz; Barros, Rui Carneiro

    2016-11-01

    In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.

  11. Steel pipe's fixed-length cutting control system based on fuzzy self-adaptive PID%基于模糊自适应PID的钢管定长切割控制系统

    Institute of Scientific and Technical Information of China (English)

    盛强

    2011-01-01

    Aiming at the weak points of traditional steel pipe's fixed-length cutting control method on flying-saw machine,a control strategy based on the fuzzy self-adaptive PID method was proposed. The inputs required by the control model are fixed-length cutting error and the error change rate,and the model gives the correct additional setpoint for the three parameters of PID control individually. Thus the on-line automatic adjustment of the PID parameters was achieved. The fuzzy self-adaptive PID control was compared with traditional PID by simulation through Matlab. The results indicate that the steel pipe's fixed-length cutting dynamic control performs much better by using the fuzzy self-adaptive PID control strategy.%针对钢管定尺飞锯传统定长切割控制策略存在的问题,提出了采用模糊自适应PID的控制策略.控制器输入取钢管的定长切割偏差e和偏差变化率ec,输出取PID控制器3个参数的修正量,从而实现了PID参数的在线自整定.采用Matlab语言,进行了常规PID控制与模糊自适应PID控制动态性能的仿真比较.研究结果表明,采用模糊自适应PID控制可明显提高定尺飞锯定长切割控制系统的动态性能.

  12. Adaptively managing wildlife for climate change: a fuzzy logic approach.

    Science.gov (United States)

    Prato, Tony

    2011-07-01

    Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple time periods.

  13. Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application

    Institute of Scientific and Technical Information of China (English)

    JIANG Ai-hua; MEI Chi; E Jia-qiang; SHI Zhang-ming

    2010-01-01

    In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.

  14. Simulation of Fuzzy Inductance Motor using PI Control Application

    Directory of Open Access Journals (Sweden)

    S.V.Halse

    2013-06-01

    Full Text Available Fuzzy control has been widely used in industrial controls, particularly in situations where conventional control design techniques have been difficult to apply. Number of fuzzy rules is very important for real time fuzzy control applications. This study is motivated by the increasing need in the industry to design highly reliable, efficiency and low complexity controllers. The proposed fuzzy controller is constructed by several fuzzy controllers with less fuzzy rules to carry out control tasks. Performances of the proposed fuzzy controller are investigated and compared to those obtained from the conventional fuzzy controller. Fuzzy logic control method has the ability to handle errors in control operation with system nonlinearity and its performance is less affected by system parameter variations.

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

    Directory of Open Access Journals (Sweden)

    Syed Zulqadar Hassan

    2017-03-01

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

  16. 基于自适应模糊滑模控制的船舶航向控制器设计%Controller Design Based on Adaptive Fuzzy Sliding Mode Control for Ship Course

    Institute of Scientific and Technical Information of China (English)

    刘文江; 隋青美; 周风余; 肖海荣

    2012-01-01

    Considering the nonlinear characteristics, modeling uncertainties and external disturbances such as wind, wave and flow in ship motion systems, an adaptive fuzzy sliding mode control (AFSMC) technology is presented to solve the ship course control problem. The fuzzy logic system is used to approximate the unknown system function and the adaptive fuzzy sliding mode controller is designed by combining sliding mode control technology with adaptive fuzzy control technology. The chattering problem of sliding mode control is relieved by adopting PI (proportional-integral) control instead of sliding mode control switching items within the boundary layer. Based on the Lyapunov function, it is theoretically proved that the controller makes all signals in the system of ship motion uniformly bounded, and using Barbalat's lemma, the tracking errors converge to zero. Simulation experiments on the course keeping and change in the presence of parameter perturbation and environment disturbances are conducted, and similar output responses with those under no perturbation and no interference circumstances by the controller based on AFSMC are obtained. The experiment results show that the proposed controller can handle system uncertainties and external disturbances effectively, and has superior controlling performance and strong robustness.%针对船舶运动系统中固有的非线性、模型不确定性和风、浪、流等的干扰.提出了自适应模糊滑模控制(AFSMC)策略解决船舶的航向控制问题.通过采用模糊逻辑系统逼近系统未知函数,将滑模控制技术与自适应模糊控制技术相结合,设计了船舶航向AFSMC控制器.在滑模边界层内应用PI (proportional-integral)控制代替滑模控制中的切换项,削弱了滑模控制带来的抖振现象.借助李亚普诺夫函数证明了船舶运动系统中的信号都一致有界并利用Barbalat引理证明了跟踪误差渐近收敛到零.在参数摄动和外界干扰情况下进

  17. Design New Robust Self Tuning Fuzzy Backstopping Methodology

    OpenAIRE

    Omid Avatefipour; Farzin Piltan; Mahmoud Reza Safaei Nasrabad; Ghasem Sahamijoo; Alireza Khalilian

    2014-01-01

    This research is focused on proposed Proportional-Integral (PI) like fuzzy adaptive backstopping fuzzy algorithms based on Proportional-Derivative (PD) fuzzy rule base with the adaptation laws derived in the Lyapunov sense. Adaptive SISO PI like fuzzy adaptive backstopping fuzzy method has two main objectives; the first objective is design a SISO fuzzy system to compensate for the model uncertainties of the system, and the second objective is focused on the design PI like fuzzy controller bas...

  18. Fuzzy logic and genetic algorithms for intelligent control of structures using MR dampers

    Science.gov (United States)

    Yan, Gang; Zhou, Lily L.

    2004-07-01

    Fuzzy logic control (FLC) and genetic algorithms (GA) are integrated into a new approach for the semi-active control of structures installed with MR dampers against severe dynamic loadings such as earthquakes. The interactive relationship between the structural response and the input voltage of MR dampers is established by using a fuzzy controller rather than the traditional way by introducing an ideal active control force. GA is employed as an adaptive method for optimization of parameters and for selection of fuzzy rules of the fuzzy control system, respectively. The maximum structural displacement is selected and used as the objective function to be minimized. The objective function is then converted to a fitness function to form the basis of genetic operations, i.e. selection, crossover, and mutation. The proposed integrated architecture is expected to generate an effective and reliable fuzzy control system by GA"s powerful searching and self-learning adaptive capability.

  19. Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.

    Science.gov (United States)

    Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu

    2015-05-01

    This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems.

  20. 一类具有未知死区MIMO系统的自适应模糊控制%Adaptive Fuzzy Control for a Class of MIMO Nonlinear Systems with Unknown Dead-zones

    Institute of Scientific and Technical Information of China (English)

    张天平; 裔扬

    2007-01-01

    A design scheme of adaptive fuzzy controller for a class of uncertain MIMO nonlinear systems with unknown deadzones and a triangular control structure is proposed in this paper. The design is based on the principle of sliding mode control and the property of Nussbaum function. The approach does not require a priori knowledge of the signs of the control gains and the upper bounds and lower bounds of dead-zone parameters to be known a priori. By introducing the integral-type Lyapunov function and adopting the adaptive compensation term of the upper bound of the optimal approximation error and the deadzone disturbance, the closed-loop control system is proved to be semi-globally stable in the sense that all signals involved are bounded, with tracking errors converging to zero.

  1. 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.

  2. Adaptive Fuzzy Control for BLDCM in Near Space Based on RBF Neural Network Compensation%基于RBF网络补偿的近空间用BLDCM自适应模糊控制

    Institute of Scientific and Technical Information of China (English)

    雷金莉; 窦满峰

    2014-01-01

    Because of the environmental parameters transformation, the parameters perturbation and load torque disturbances of the brushless direct current motor ( BLDCM) in near space will appear, and the response speed and stability of control system will be bad. To solve this problem, we propose an adaptive fuzzy control algorithm based on RBF( radial basis function) neural network compensation. The adaptive fuzzy controller is deduced to ensure the BLDCM system has good dynamic performance, the RBF neural network is adopted to do online identification and compensate for the speed error when the parameters perturbation and load torque disturbance appear in order to a-chieve the purposes of fast response speed and good robustness. Comparing the simulation results of adaptive fuzzy control with those of RBF neural network compensation and adaptive fuzzy control, we show preliminarily that:(1) the adaptive fuzzy control Based on RBF neural network has a strong robustness against the uncertainties of the BLDCM;(2) its response time is shorten by adaptive fuzzy control over 10ms;(3) its peak electromagnetic torque is decreased about 20% during the response process.%近空间用无刷直流电机( BLDCM)受环境参数影响出现不确定性参数摄动和负载扰动,系统的控制性能降低。为消除不确定性因素的影响,提出了一种基于RBF网络补偿的自适应模糊控制算法。该控制算法是在自适应模糊控制的基础上,引入RBF网络补偿控制器,对参数摄动和负载转矩突变引起的转速误差进行在线辨识和动态补偿,以达到快速鲁棒自适应控制目的。对比具有RBF网络补偿的自适应模糊控制和自适应模糊控制的模拟仿真实验结果表明:在转速变化、负载转矩突变和转动惯量改变条件下,有RBF网络补偿控制的响应时间缩短了10 ms以上,响应过程中,电磁转矩的瞬时峰值减少了20%左右,对近空间BLDCM系统的不确定性鲁棒性强。

  3. A new robust fuzzy method for unmanned flying vehicle control

    Institute of Scientific and Technical Information of China (English)

    Mojtaba Mirzaei; Mohammad Eghtesad; Mohammad Mahdi Alishahi

    2015-01-01

    A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles (UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control (IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.

  4. An Adaptive Neuro-Fuzzy Inference Distributed Power Flow Controller (DPFC In Multi-Machine Power Systems

    Directory of Open Access Journals (Sweden)

    Gurrala Madhusudhan Rao

    2014-10-01

    Full Text Available Abstract: The main theme of the paper which deals with the enhancing steady-state and dynamics performance of the power grids by Flexible AC Transmission System (FACTS based on computational intelligence. The proposed technique will be applied to solve real problems in a power grid. The FACTS device, which will be used in the paper, is the most promising one, which known as the Distributed Power Flow Controller (DPFC. The paper achieves the optimization of the type, the location and the size of the power and control elements for DPFC to optimize the system performance. The paper derives the criteria to install the DPFC in an optimal location with optimal parameters and then designs an AI based damping controller for enhancing power system dynamic performance. In this paper, for every operating point genetic algorithm is used to search for controllers’ parameters, parameters found at certain operating point are different from those found at others. ANFISs are required in this case to recognize the appropriate parameters for each operating point.

  5. Methodology for adapting the parameters of a fuzzy system using the extended Kalman filter

    OpenAIRE

    2011-01-01

    When we try to analyze and to control a system whose model was obtained only based on input/output data, accuracy is essential in the model. On the other hand, to make the procedure practical, the modeling stage must be computationally efficient. In this regard, this paper presents the application of extended Kalman filter for the parametric adaptation of a fuzzy model.

  6. MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System

    Science.gov (United States)

    2015-08-02

    16. SECURITY CLASSIFICATION OF: 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12. DISTRIBUTION AVAILIBILITY STATEMENT 6...Instance AdaptiveNeuro-Fuzzy Inference System We introduce a novel adaptive neuro -fuzzy architecture based on the framework of Multiple Instance Fuzzy...Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS

  7. A Fuzzy Control Irrigation System For Cottonfield

    Science.gov (United States)

    Zhang, Jun; Zhao, Yandong; Wang, Yiming; Li, Jinping

    A fuzzy control irrigation system for cotton field is presented in this paper. The system is composed of host computer, slave computer controller, communication module, soil water sensors, valve controllers, and system software. A fuzzy control model is constructed to control the irrigation time and irrigation quantity for cotton filed. According to the water-required rules of different cotton growing periods, different irrigation strategies can be carried out automatically. This system had been used for precision irrigation of the cotton field in Langfang experimental farm of Soil and Fertilizer Institute, Chinese Academy of Agricultural Sciences in 2006. The results show that the fuzzy control irrigation system can improve cotton yield and save much water quantity than the irrigation system based on simple on-off control algorithm.

  8. Variable-order fuzzy fractional PID controller.

    Science.gov (United States)

    Liu, Lu; Pan, Feng; Xue, Dingyu

    2015-03-01

    In this paper, a new tuning method of variable-order fractional fuzzy PID controller (VOFFLC) is proposed for a class of fractional-order and integer-order control plants. Fuzzy logic control (FLC) could easily deal with parameter variations of control system, but the fractional-order parameters are unable to change through this way and it has confined the effectiveness of FLC. Therefore, an attempt is made in this paper to allow all the five parameters of fractional-order PID controller vary along with the transformation of system structure as the outputs of FLC, and the influence of fractional orders λ and μ on control systems has been investigated to make the fuzzy rules for VOFFLC. Four simulation results of different plants are shown to verify the availability of the proposed control strategy.

  9. Terminology and concepts of control and Fuzzy Logic

    Science.gov (United States)

    Aldridge, Jack; Lea, Robert; Jani, Yashvant; Weiss, Jonathan

    1990-01-01

    Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.

  10. FPGA Fuzzy Controller Design for Magnetic Ball Levitation

    Directory of Open Access Journals (Sweden)

    Basil Hamed

    2012-09-01

    Full Text Available this paper presents a fuzzy controller design for nonlinear system using FPGA. A magnetic levitation system is considered as a case study and the fuzzy controller is designed to keep a magnetic object suspended in the air counteracting the weight of the object. Fuzzy controller will be implemented using FPGA chip. The design will use a high-level programming language HDL for implementing the fuzzy logic controller using the Xfuzzy tools to implement the fuzzy logic controller into HDL code. This paper, advocates a novel approach to implement the fuzzy logic controller for magnetic ball levitation system by using FPGA.

  11. Adaptive Fuzzy PID Based Control Strategy For 3Phase 4Wire Shunt Active Filter To Mitigate Current Harmonics Of Grid Interconnection Of Renewable Energy Based Distribution System

    Directory of Open Access Journals (Sweden)

    Kaleemullah

    2016-06-01

    Full Text Available This paper presents a new control strategy for controlling the shunt active power filter to compensate reactive power and to reduce the unwanted harmonics in the grid current. Shunt active filter act as a current source which is connected in parallel with a non-linear load and controlled to produce the required compensating current. The proposed control strategy is based on the fuzzy PID controller which is used for determining the reference compensating currents of the three-phase shunt active power filters. Simulations are carried out using MATLAB/SIMULINK to verify the performance of the proposed controller. The output shows the controller has fast dynamic response high accuracy of tracking DC voltage reference and robust to load parameters variations.

  12. Fuzzy Sliding Mode Control for Discrete Nonlinear Systems

    Institute of Scientific and Technical Information of China (English)

    F.Qiao.Q.M.Zhu; A.Winfield; C.Melhuish

    2003-01-01

    Sliding mode control is introduced into classical model free fuzzy logic control for discrete time nonlinear systems with uncertainty to the design of a novel fuzzy sliding mode control to meet the requirement of necessary and sufficient reaching conditions of sliding mode control. The simulation results show that the proposed controller outperforms the original fuzzy sliding mode controller and the classical fuzzy logic controller in stability, convergence and robustness.

  13. Application of fuzzy self-adaption PI control on brushless DC motor speed regulating system%模糊自适应PI控制在直流无刷电动机调速系统的应用

    Institute of Scientific and Technical Information of China (English)

    胡发焕; 唐军; 邱小童

    2012-01-01

    在分析直流无刷电动机的工作原理和数学模型的基础上,提出基于速度环和电流环的双环控制模式,在速度环中采用模糊自适应PI控制方式,在电流环中采用传统的PID控制方式.实验表明,与传统PID控制方式相比,采用模糊自适应PI控制,在负载或参数发生变化时,具有转速波动更小、鲁棒性更强的特点,是一种更优越的控制方法.%Based on analyzing the principle and mathematical model of the brushless DC motor, the paper proposes a control model on the basis of speed loop and current loop, self-adaption fuzzy PI control model is a-dopted in the speed loop, and traditional PID control model is used in the current loop. It is showed that when the load or parameters vary, comparing to traditional PID control model, the self-adaption fuzzy PI control model gets less fluctuation and better robustness.

  14. Adaptive Fuzzy Tension Control System for Winding Process of the Film Materials%薄膜材料的绕组处理的适应失真压力的控制系统

    Institute of Scientific and Technical Information of China (English)

    苏国和; 陈自雄

    2008-01-01

    薄膜材料的绕组处理是在一个高度非线性的动态系统中维持应力不变.提出一个为薄膜材料的绕组处理在不同摩擦锟供料速度下的适应模糊应力的控制系统.该提出的适应模糊应力的控制系统包括一个模糊应力控制器和一个适应调谐器.模糊应力控制器是主进度控制器,一个平移宽度的概念和变化模式技术被包括在模糊推论中以矫正模糊现象,而且只有一个参数因素需要被调整.为了对抗在实际应用中的不确定,一个失真压力的控制系统占据着简单控制框架,无震颤的,稳定跟踪性能和对不确定性的鲁棒的优势.与传统的比例积分应力控制方法相比较可提出的这种控制方法有显著的优势.%The winding process of the film materials is in a highly nonlinear dynamic system to maintain ten-sion at constant value. An adaptive fuzzy tension control (AFTC) architecture is proposed for winding process of the film materials under various feeding speeds at pinch rolls. The proposed AFTC architecture comprises a fuzzy tension controller and an adaptive tuner. The fuzzy tension controller (FTC) is the main tracking controller, in which a translation-width idea and sliding-mode technology are embedded into fuzzy inference engine to remedy chattering phenomena and only one parameter needs adjustment. To confront the uncertainties existed in practical applications, an adaptive tuner is added to adjust the parameter for further assuring robust and optimal performance. The winding process of the film materials with the AF- TC architecture possesses the salient advantages of simple control framework, free from chattering, stable tracking performance and robust to uncertainties. The advantages of the proposed control method are indi-cated in comparison with traditional proportional-integral tension control (PITC) method.

  15. Implementation of a Fuzzy Logic Speed Controller for a Permanent ...

    African Journals Online (AJOL)

    Journal of Research in National Development. Journal Home ... Fuzzy logic controlled model of the DC motor was implemented. The purpose is to ... the proposed strategy. Keywords: Brushless DC motor, fuzzy logic control, speed controller ...

  16. Automobile active suspension system with fuzzy control

    Institute of Scientific and Technical Information of China (English)

    刘少军; 黄中华; 陈毅章

    2004-01-01

    A quarter-automobile active suspension model was proposed. High speed on/off solenoid valves were used as control valves and fuzzy control was chosen as control method . Based on force analyses of system parts, a mathematical model of the active suspension system was established and simplified by linearization method. Simulation study was conducted with Matlab and three scale coefficients of fuzzy controller (ke, kec, ku) were acquired. And an experimental device was designed and produced. The results indicate that the active suspension system can achieve better vibration isolation performance than passive suspension system, the displacement amplitude of automobile body can be reduced to 55%. Fuzzy control is an effective control method for active suspension system.

  17. 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)

  18. 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.

  19. Fuzzy Self-Adaptation of Mission-Critical Software Under Uncertainty

    Institute of Scientific and Technical Information of China (English)

    Qi-Liang Yang; Jian Lv; Xian-Ping Tao; Xiao-Xing Ma; Jian-Chun Xing; Wei Song

    2013-01-01

    Mission-critical software (MCS) must provide continuous,online services to ensure the successful accomplishment of critical missions.Self-adaptation is particularly desirable for assuring the quality of service (QoS) and availability of MCS under uncertainty.Few techniques have insofar addressed the issue of MCS self-adaptation,and most existing approaches to software self-adaptation fail to take into account uncertainty in the self-adaptation loop.To tackle this problem,we propose a fuzzy control based approach,i.e.,Software Fuzzy Self-Adaptation (SFSA),with a view to deal with the challenge of MCS self-adaptation under uncertainty.First,we present the SFSA conceptual framework,consisting of sensing,deciding and acting stages,and establish the formal model of SFSA to lay a rigorous and mathematical foundation of our approach.Second,we develop a novel SFSA implementation technology as well as its supporting tool,i.e.,the SFSA toolkit,to automate the realization process of SFSA.Finally,we demonstrate the effectiveness of our approach through the development of an adaptive MCS application in process control systems.Validation experiments show that the fuzzy control based approach proposed in this work is effective and with low overheads.

  20. Synchronization of Uncertain Time Delay Chaotic Systems using the Adaptive Fuzzy Method

    Institute of Scientific and Technical Information of China (English)

    关新平; 华长春

    2002-01-01

    We consider the synchronization problem of a class of first-order differential-delay chaotic systems. We utilize time-delay fuzzy logic systems to approximate continuous nonlinear time-delay functions, so that the precise mathematical model need not be known. Adopting the adaptive fuzzy control method, we construct a class of state feedback controllers which can render the closed-loop error systems to be asymptotically stable. We carry out simulations of synchronizing Mackey-Glass and logistic chaotic systems, and the results are reasonable.

  1. Exploration of the Adaptive Neuro - Fuzzy Inference System Architecture and its Applications

    Directory of Open Access Journals (Sweden)

    Okereke Eze Aru

    2016-09-01

    Full Text Available In this paper we exhibited an architecture and essential learning process basic in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of adaptive network. In genuine figuring environment, soft computing techniques including neural network, fuzzy logic algorithms have been generally used to infer a real choice utilizing given input or output information traits, ANFIS can build mapping taking into account both human learning and hybrid algorithms. This study includes investigation of ANFIS methodology. ANFIS procedure is utilized to display nonlinear functions, to control a standout amongst the most essential parameters of the impelling machine and anticipate a turbulent time arrangement, all yielding more viable, quicker result.

  2. 模型不确定非线性系统的自适应模糊Backstepping预测控制%Adaptive fuzzy backstepping predictive control for a class for nonlinear systems with model uncertainty

    Institute of Scientific and Technical Information of China (English)

    郑兰; 周卫东; 廖成毅; 程华

    2014-01-01

    为解决一类模型不确定严格反馈非线性系统的跟踪控制问题,提出一种使闭环系统稳定且滚动时域性能指标在线最小化的自适应模糊反步预测控制策略。模糊系统用来逼近该设计过程中的未知非线性项,自适应参数直接用来估计最优逼近权值向量范数的平方,从而只有一个自适应参数需要在线调节;同时考虑模糊基函数的性质,所设计的控制律与自适应律均不含模糊基函数项,理论证明该方法设计的控制器保证闭环系统所有信号是半全局有界的,并且跟踪误差收敛于零的某一邻域。该方法所设计的控制器形式简单,计算量小,更易于实际应用,仿真算例验证提出算法的有效性。%To overcome the tracking control problem for a class of strict⁃feedback nonlinear system with model uncertain , an adaptive fuzzy backstepping predication control algorithm which can make the closed⁃loop system stable and minimize the receding horizon guaranteed cost on⁃line is proposed. Fuzzy logic systems are employed to approximate the unknown term in the design process. As the adaptive parameter are directly used to estimate the norm of the optimal approximation weight vector, only one parameter need to be tuned on⁃line. Considering the property of the fuzzy basis function, the designed control laws and adaptive laws do not contain the fuzzy basis function term. Theoretically, it is proved that the using the constructed controller can guarantee that all signals in closed⁃loop are semi⁃globally uniformly ultimately bounded, and the tracking error convergence to a small neighborhood of the origin. As the form of the controller designed in this way is simplicity and the computation is small, this control strategy is easily realized in practice. Finally, the simulation results demonstrate the feasibility of the proposed scheme.

  3. 基于RBF神经网络辨识的直接甲醇燃料电池电堆非成性建模与自适应模糊控制%Nonlinear modeling based on RBF neural networks identification and adaptive fuzzy control of DMFC stack

    Institute of Scientific and Technical Information of China (English)

    苗青; 曹广益; 朱新坚

    2006-01-01

    The temperature models of anode and cathode of direct methanol fuel cell (DMFC) stack were established by using radial basis function (RBF) neural networks identification technique to deal with the modeling and control problem of DMFC stack. An adaptive fuzzy neural networks temperature controller was designed based on the identification models established, and parameters of the controller were regulated by novel back propagation (BP) algorithm. Simulation results show that the RBF neural networks identification modeling method is correct, effective and the models established have good accuracy. Moreover, performance of the adaptive fuzzy neural networks temperature controller designed is superior.

  4. Study of Fuzzy Adaptive PID Control of Brushless DC Motor%无刷直流电机模糊自适应PID控制的研究

    Institute of Scientific and Technical Information of China (English)

    潘晓磊; 赵川; 吕海立

    2016-01-01

    为了提高无刷直流电机控制系统的动、静态性能,将模糊控制结合PID控制算法应用到无刷直流电机速度控制系统中。在分析了无刷直流电机速度控制系统的基础上,利用PSIM与MATLAB/Simulink共同建立了无刷直流电动机模糊自适应PID控制的仿真模型,充分发挥了PSIM和MATLAB/Simulink各自在仿真方面的优势,简化了建立仿真模型的过程。仿真结果表明,采用模糊PID集成控制算法能够使无刷直流电机速度控制系统具有更快的响应速度和更强的抗干扰能力,对无刷直流电机控制系统的设计具有一定的指导意义。%In order to improve the static and dynamic performance of brushless DC motor control system, fuzzy control combined with PID control algorithm is applied to the brushless DC motor speed control system, the integration of fuzzy PID control algorithm. On the analysis of the brushless dc motor speed control system, using the MATLAB/Simulink to establish brushless DC motor speed control system simulation model. Among them, the control system of brushless DC motor drive model and control algorithm were established in PSIM and MATLAB/Simulink environment, give full play to their advantages, the PSIM and MATLAB simplifies the process of establishing simulation model. The simulation results show that the integration of fuzzy PID control algorithm can make the brushless DC motor speed control system has better dynamic and static performance and strong anti-jamming capability, the design of brushless DC motor control system has a certain guiding significance.

  5. Age Estimation Based on CLM, Tree Mixture With Adaptive Neuron Fuzzy, Fuzzy Svm

    Directory of Open Access Journals (Sweden)

    Mohammad Saber Iraji

    2014-02-01

    Full Text Available As you know, age diagnosis based on the image is one of the most attractive topics in computer .In this paper, we present a intelligent model to estimate the age of face image. We use shape and texture feature extraction from FG-NET landmark image data set using AAM(Active Appearance Model, CLM (Constrained Local Model, tree Mixture algorithms. Finally, the obtained features were given as the training data to the ANFIS (adaptive neuro fuzzy influence system, FSVM (Fuzzy Support Vector Machine. Our experimental results show that In our proposed system, fuzzy svm has less errors and system worked more accurate and appropriative than prior methods. Our system is able to identify age of face image from different directions as is.

  6. Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation

    Directory of Open Access Journals (Sweden)

    Auday Al-Mayyahi

    2014-11-01

    Full Text Available This article proposes an adaptive neuro-fuzzy inference system (ANFIS for solving navigation problems of an autonomous ground vehicle (AGV. The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD; right distance (RD and left distance (LD for the low-level motion control. Two heading controllers deploy the angle difference (AD between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles.

  7. Application of intelligent fuzzy adaptive PID algorithm in temperature control system of polymerization reactor%智能模糊自适应PID在化学反应釜温度控制系统中的应用

    Institute of Scientific and Technical Information of China (English)

    吴剑威; 孔慧芳; 唐立新

    2013-01-01

    The temperature control system of polymerization reactor has characteristics of time-varying,nonlinearity and lag,and it is difficult to create the mathematical model and get the parameters precisely,which seriously affects the rapidity and robustness of temperature control system.An intelligent fuzzy adaptive PID (IFA-PID) control algorithm is proposed in order to solve the problems.The fuzzy control is used when the error is large and the fuzzy adaptive PID (FA-PID) control with independent differentiation is used when the error is small.Furthermore,a smooth handoff is achieved by switching function between the two control modes,thus the complement of the advantages andorganic combination has been realized.Then the simulation model is created in Simulink toolbox of Matlab for simulation comparison experiment of traditional PID control、FA-PID control and IFA-PID control under model matching and model mismatch.Simulation results show IFA-PID is better than the other two algorithms in overshoot,settling time and other performance index,in addition,it has better adaptability and stability,and it can improve the quality characteristics and control precision of system.The presented algorithm can effectively overcome the model mismatch and parameters variation,which is very suitable for the control of nonlinear system with time-varying delay.%化学反应釜温度控制系统具有时变性、非线性、滞后性等特点,数学模型和参数难以精确得到,严重影响了温度控制的快速性和鲁棒性.为解决这些问题,设计了一种智能模糊自适应PID(Intelligent Fuzzy Adaptive PID,IFA-PID)控制算法,当误差较大时采用模糊控制,误差较小时采用微分独立模糊自适应PID(Fuzzy Adaptive PID,FA-PID)控制,通过切换函数实现两种控制方式的平滑切换,实现了2种控制方法的有机结合和优势互补.利用MATLAB的Simulink仿真工具箱建立模型,并做了传统PID、FA-PID以及IFA-PID在

  8. Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

    Institute of Scientific and Technical Information of China (English)

    Anwar M. Mirza; Asmatullah Chaudhry; Badre Munir

    2007-01-01

    We present a general formulation based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Gray-level images degraded with Gaussian white noise have been considered. Based on the pixel local neighborhood, fuzzy logic has been employed intelligently to avoid unnecessary estimation of a pixel. The intensity estimation of the selected pixels is then carried out by employing punctual kriging in conjunction with the method of Lagrange multipliers and estimates of local semi-variances. Application of such a hybrid technique performing both selection and intensity estimation of a pixel demonstrates substantial improvement in the image quality as compared to the adaptive Wiener filter and existing fuzzy- kriging approaches. It has been found that these filters achieve noise reduction without loss of structural detail information, as indicated by their higher structure similarity indices, peak signal to noise ratios and the new variogram based quality measures.

  9. A complex control system based on the fuzzy PID control and state predictor feedback control

    Institute of Scientific and Technical Information of China (English)

    Zhengxi Li; Jie Liu; Dehui Sun; Rentao Zhao

    2004-01-01

    A multi-mode adaptive controller was proposed. The controller features in the combination of Bang-bang and Fuzzy PID controls with state predictor. When large error exists, the controller operates in Bang-bang mode, otherwise it works as a fuzzy PID controller. For only few parameters to be adjusted, the real time controlled system achieveed good stability and fast response. Furthermore, the introduction of state observer was also discussed to extend the capability of the proposed controller to the plant with time-delay factors. The classical PID controller and the multi-mode controller were applied to the same second-order system successively. By comparison of the simulation results, the effectiveness of the controller were shown. At last, on electric-wire production line, this approach was practiced to control electric-wire diameter with an additive random disturbance signal. The test result further proved the effectiveness of the multi-mode controller.

  10. Fuzzy Control of Model Travel Tracking for Vehicle Semi-Active Suspension

    Institute of Scientific and Technical Information of China (English)

    GUAN Ji-fu; WU Yun-peng; GU Liang; HUANG Hua

    2006-01-01

    The control strategy of the model travel tracking for the vehicle suspension system is presented based on analyzing the responses of the vehicle suspension travel. A fuzzy control system of vehicle suspension is designed, in which the suspension travel output of the adaptive LQG control system is taken as the tracking objective. The simulation results prove that the suspension travel and vertical acceleration can be tracked simultaneously with the simple fuzzy controller,and the tracking effect of fuzzy control is better than that of the PID controller.

  11. Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

    2017-07-01

    The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system. The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset. The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069. The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H∞ Filter

    Directory of Open Access Journals (Sweden)

    Fariz Outamazirt

    2014-09-01

    Full Text Available Although nonlinear H∞ (NH∞ filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞ filter is proposed for the Unmanned Aerial Vehicle (UAV localization problem. Based on a real-time Fuzzy Inference System (FIS, the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds  and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.

  13. Intelligent control based on fuzzy logic and neural net theory

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  14. Adaptive synchronization of T-S fuzzy chaotic systems with unknown parameters

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jae-Hun [Department of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Sudaemoon-gu, Seoul 120-749 (Korea, Republic of)]. E-mail: jhkim@yeics.yonsei.ac.kr; Park, Chang-Woo [Precision Machinery Research Center, Korea Electronics Technology Institute, 203-103 B/D 192, Yakdae-dong, Wonmi-gu, Puchon-si, Kyunggi-do 420-140 (Korea, Republic of); Kim, Euntai [Department of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Sudaemoon-gu, Seoul 120-749 (Korea, Republic of); Park, Mignon [Department of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Sudaemoon-gu, Seoul 120-749 (Korea, Republic of)

    2005-06-01

    This paper presents a fuzzy model-based adaptive approach for synchronization of chaotic systems which consist of the drive and response systems. Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, we design the response system that estimates the parameters of the drive system by adaptive strategy. The adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. In addition, the controller in the response system contains two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples, including Duffing oscillator and Lorenz attractor, are given to demonstrate the validity of the proposed adaptive synchronization approach.

  15. Fuzzy Vibration Control of a Smart Plate

    Science.gov (United States)

    Muradova, Aliki D.; Stavroulakis, Georgios E.

    2013-04-01

    Vibration suppression of a smart thin elastic rectangular plate is considered. The plate is subjected to external disturbances and generalized control forces, produced, for instance, by electromechanical feedback. A nonlinear controller is designed, based on fuzzy inference. The initial-boundary value problem is spatially discretized by means of the time spectral method. The implicit Newmark-beta method is employed for time integration. Two numerical algorithms are proposed. The techniques have been implemented within MATLAB with the use of the Fuzzy Logic Toolbox. Representative numerical results are given.

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

    Directory of Open Access Journals (Sweden)

    Yong-Kun Lu

    2015-02-01

    Full Text Available 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 stability of the controller is analyzed by Lyapunov stability theory. The effectiveness of the proposed speed regulator is demonstrated by the simulation results in comparison with the conventional integral sliding-mode controller based on boundary layer.

  17. Uncovering transcriptional interactions via an adaptive fuzzy logic approach

    Directory of Open Access Journals (Sweden)

    Chen Chung-Ming

    2009-12-01

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

  18. Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

    Institute of Scientific and Technical Information of China (English)

    Jayant P. Sangole; Gopal R. Patil

    2014-01-01

    Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun-tries. Intersections with no specific priority to any move-ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom-etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neuro-fuzzy interface system (ANFIS) to the modeling of gap acceptance behavior of right-turning vehicles at limited priority T-intersections (in India, vehicles are driven on the left side of a road). Field data are collected using video cameras at four T-intersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver’s decision (accepted/rejected). ANFIS models are developed by using 80% of the extracted data (total data observations for major road right-turning vehicles are 722 and 1,066 for minor road right-turning vehicles) and remaining are used for model vali-dation. Four different combinations of input variables are considered for major and minor road right turnings sepa-rately. Correct prediction by ANFIS models ranges from 75.17% to 82.16% for major road right turning and 87.20% to 88.62% for minor road right turning. The models developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

  19. 基于模糊逻辑的一类非线性系统直接自适应控制%Direct Adaptive Control of a Class of Nonlinear SystemBased on Fuzzy Logic

    Institute of Scientific and Technical Information of China (English)

    朴营国; 张俊星; 张化光

    2001-01-01

    On the basis of the fuzzy logic, a direct adaptive trackingcontrol architecture is developed for a class of nonlinear dynamic systems. In the procedure, the controller is composed of fuzzy approximate controller (FAC) and fuzzy sliding mode compensating controller (FSMCC), The FAC is used to approximate the optimal controller globally, and the FSMCC is designed globally for compensating the approximation errors and uncertainties of systems, and meanwhile attenuating the external disturbance. Global asymptotic stability of the whole closed control system is obtained in the Lyapunov Sense, with tracking errors convergency to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed algorithm.%针对一类连续非线性不确定系统,基于模糊逻辑提出了一种新的自适应跟踪控制方法.在此方法中,控制器由两部分组成:模糊逼近控制器(FAC)和模糊滑模补偿控制器(FSMCC).其中,FAC利用模糊逻辑系统全局逼近理想控制器,FSMCC用于全局补偿逼近误差和系统的不确定性及消除外部干扰的影响.整个闭环控制系统在Lyapunov意义下全局渐进稳定且系统的跟踪误差收敛于零的某一邻域内.最后通过示例验证了本方法的有效性.

  20. A new fuzzy sliding mode controller for vibration control systems using integrated-structure smart dampers

    Science.gov (United States)

    Dzung Nguyen, Sy; Kim, Wanho; Park, Jhinha; Choi, Seung-Bok

    2017-04-01

    Vibration control systems using smart dampers (SmDs) such as magnetorheological and electrorheological dampers (MRD and ERD), which are classified as the integrated structure-SmD control systems (ISSmDCSs), have been actively researched and widely used. This work proposes a new controller for a class of ISSmDCSs in which high accuracy of SmD models as well as increment of control ability to deal with uncertainty and time delay are to be expected. In order to achieve this goal, two formualtion steps are required; a non-parametric SmD model based on an adaptive neuro-fuzzy inference system (ANFIS) and a novel fuzzy sliding mode controller (FSMC) which can weaken the model error of the ISSmDCSs and hence provide enhanced vibration control performances. As for the formulation of the proposed controller, first, an ANFIS controller is desgned to identify SmDs using the improved control algorithm named improved establishing neuro-fuzzy system (establishing neuro-fuzzy system). Second, a new control law for the FSMC is designed via Lyapunov stability analysis. An application to a semi-active MRD vehicle suspension system is then undertaken to illustrate and evaluate the effectiveness of the proposed control method. It is demonstrated through an experimental realization that the FSMC proposed in this work shows superior vibration control performance of the vehicle suspension compared to other surveyed controller which have similar structures to the FSMC, such as fuzzy logic and sliding mode control.

  1. The Optimized Disign of the Fuzzy Controller(Ⅰ)--The predigested disquisition of rules of fuzzy control

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    To improve the ability and precisions of the fuzzy control,this thesis points out the adjusted fuzzy control method,realizes the precision of the fuzzy quantity, and reduces the number of the fuzzy control rules,so that it can predigest the process of disigns and realize the methods without influencing the idiocratic control,which are on the base of the domain flexing.

  2. Z Number Based Fuzzy Inference System for Dynamic Plant Control

    Directory of Open Access Journals (Sweden)

    Rahib H. Abiyev

    2016-01-01

    Full Text Available Frequently the reliabilities of the linguistic values of the variables in the rule base are becoming important in the modeling of fuzzy systems. Taking into consideration the reliability degree of the fuzzy values of variables of the rules the design of inference mechanism acquires importance. For this purpose, Z number based fuzzy rules that include constraint and reliability degrees of information are constructed. Fuzzy rule interpolation is presented for designing of an inference engine of fuzzy rule-based system. The mathematical background of the fuzzy inference system based on interpolative mechanism is developed. Based on interpolative inference process Z number based fuzzy controller for control of dynamic plant has been designed. The transient response characteristic of designed controller is compared with the transient response characteristic of the conventional fuzzy controller. The obtained comparative results demonstrate the suitability of designed system in control of dynamic plants.

  3. Indirect,adaptive,fuzzy and distributed H∞ control for multi-machine power system%多机电力系统间接自适应模糊分散H∞控制研究

    Institute of Scientific and Technical Information of China (English)

    吴忠强; 宋明厚; 付立元

    2013-01-01

    Based on the multi-machine excitation control model and the multi-variable,strong-coupling and nonlinear system characteristics,an indirect,adaptive, fuzzy and distributed H∞ tracking control scheme is proposed based on the fuzzy approximating,which constructs the fuzzy adaptive system to approximate the unknown function and designs the H∞, compensator to eliminate the external disturbance and the error of fuzzy approximating. The stable control of multi-machine power system is thus realized with H∞ property. Simulative results show that,the operational angle of generator rotor tends to a stable value and both the relative angular velocity and the tracking error tend to zero when three-phase recoverable or unrecoverable faults occurs in multi-machine power system. Compared with PSS scheme,the proposed scheme has shorter settling time and smaller overshoot.%针对多机电力系统励磁控制模型,考虑到系统的多变量、强耦合非线性等特性,提出了基于模糊逼近的间接自适应模糊分散H∞跟踪控制方案.该方案通过构建模糊自适应系统来逼近未知函数,然后设计H∞补偿器来抵消外部扰动和模糊逼近误差,从而实现了对多机电力系统的稳定性控制并且具有H∞性能.仿真结果表明,当多机电力系统发生三相可恢复故障和三相不可恢复故障时,发电机的转子运行角趋于某一固定值,相对转速和跟踪误差都趋于零.所提方案与电力系统稳定器(PSS)方案对比可知,PSS方案虽然能使系统稳定,但是其超调量大、过渡时间长,而所提方案不仅可以使系统在故障之后迅速稳定,而且超调量更小.

  4. Fuzzy Logic Control of a Ball on Sphere System

    Directory of Open Access Journals (Sweden)

    Seyed Alireza Moezi

    2014-01-01

    Full Text Available The scope of this paper is to present a fuzzy logic control of a class of multi-input multioutput (MIMO nonlinear systems called “system of ball on a sphere,” such an inherently nonlinear, unstable, and underactuated system, considered truly to be two independent ball and wheel systems around its equilibrium point. In this work, Sugeno method is investigated as a fuzzy controller method, so it works in a good state with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for such nonlinear dynamic systems. The system’s dynamic is described and the equations are illustrated. The outputs are shown in different figures so as to be compared. Finally, these simulation results show the exactness of the controller’s performance.

  5. Fuzzy Mathematics for Raw Silk Size Control

    Institute of Scientific and Technical Information of China (English)

    HU Zheng-yu; YU Hai-feng; GU Ping

    2008-01-01

    With photographing and experiments,this paper divides the cocoon layers into three categories according to their colors,establishes three-color membership function based on fuzzy mathemtics,constructs fuzzy sets which satisfy the range of size contrd by using the ordinary set and attached fiequency of three color cocoons combination,then achieves the ordinary sets of range of size control by choosing λ-cut.Under these ordinary sets,each end does duality relative level,then sets up relative matrix and overall sequence and finds the membership function to iudge whether the size cmtrol is normal.

  6. The design for the fuzzy PID control of the intelligent following vehicle with gas floating

    Institute of Scientific and Technical Information of China (English)

    He Yi; Song Xiaodong; Chen Ming

    2012-01-01

    The intelligent following vehicle with gas floating has the characteristics of complicated structure and large quality. In this paper ,the author first establish the mathematical mode of the motion system land and then design a controller using the fuzzy PID control method which could realize auto-tuning PID parameters. By the MATLAB simulation analysis, the results show that fuzzy self-tuning PID control can enhance the response speed of system and has a better adaptability.

  7. Adaptive fuzzy integral sliding mode velocity controlfor the cutting system of a trench cutte~

    Institute of Scientific and Technical Information of China (English)

    Qi-yan TIAN; Jian-hua WEI; Jin-hui FANG; Kai GUO

    2016-01-01

    This paper presents a velocity controller for the cutting system of a trench cutter (TC). The cutting velocity of a cutting system is affected by the unknown load characteristics of rock and soil. In addition, geological conditions vary with time. Due to the complex load characteristics of rock and soil, the cutting load torque of a cutter is related to the geological conditions and the feeding velocity of the cutter. Moreover, a cutter's dynamic model is subjected to uncertainties with unknown effects on its function. In this study, to deal with the particular characteristics of a cutting system, a novel adaptive fuzzy integral sliding mode control (AFISMC) is designed for controlling cutting velocity. The model combines the robust characteristics of an integral sliding mode controller with the adaptive adjusting characteristics of an adaptive fuzzy controller. The AFISMC cutting velocity con- troller is synthesized using the backstepping technique. The stability of the whole system including the fuzzy inference system, integral sliding mode controller, and the cutting system is proven using the Lyapunov theory. Experiments have been conducted on a TC test bench with the AFISMC under different operating conditions. The experimental results demonstrate that the proposed AFISMC cutting velocity controller gives a superior and robust velocity tracking performance.

  8. A Direct Feedback Control Based on Fuzzy Recurrent Neural Network

    Institute of Scientific and Technical Information of China (English)

    李明; 马小平

    2002-01-01

    A direct feedback control system based on fuzzy-recurrent neural network is proposed, and a method of training weights of fuzzy-recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simul ation results indicate that fuzzy-recurrent neural network controller has perfect dynamic and static performances .

  9. FUZZY LOGIC CONTROLLED CATHODIC PROTECTION CIRCUIT DESIGN

    OpenAIRE

    AKÇAYOL, M. Ali

    2010-01-01

    In this study, output voltage of automatic transformer-rectifier (TR) unit of impressed current cathodic protection has been controlled by using fuzzy logic controller. To prevent corrosion, voltage between the protection metal and the auxiliary anode has to be controlled on a desired level. Because soil resistance in the environment changes with humidity and soil characteristics, TRs must control the output voltage between protection metal and auxiliary anode automatically. In this study, a ...

  10. Fuzzy controller for a system with uncertain load

    DEFF Research Database (Denmark)

    Kulczycki, P.; Wisniewski, Rafal

    2002-01-01

    in engineering solutions. 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. The methodology proposed in this work may be easily adapted to other modeling......In many applications of motion control, problems associated with imprecisely measured or changing load (a mass or a moment of inertia) can be a serious obstacle in the formation of satisfactory controlling systems. This barrier compels the designer to include various kinds of uncertainties...

  11. A Laboratory Testbed for Embedded Fuzzy Control

    Science.gov (United States)

    Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.

    2011-01-01

    This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses…

  12. A Laboratory Testbed for Embedded Fuzzy Control

    Science.gov (United States)

    Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.

    2011-01-01

    This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses…

  13. Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

  14. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  15. ADAPTIVE CONTROL OF REDUNDANT VARIABLE GEOMETRY TRUSS MANIPULATORS BASED ON FUZZY NEURAL NETWORK%基于模糊神经网络的冗余度变几何桁架机器人自适应控制

    Institute of Scientific and Technical Information of China (English)

    徐礼钜; 吴江; 梁尚明

    2000-01-01

    本文提出了一种基于模糊神经网络(FNN)的机器人位置自适应控制方法.利用模糊神经网络模型来辨识冗余度变几何桁架机器人的逆动力学模型,用常规反馈控制器完成外部干扰的补偿和闭环控制.并以四重四面体变几何桁架机器人为例进行仿真计算,表明该控制方法具有良好的轨迹跟踪精度和抗干扰能力.%An adaptive control scheme for redundant variable geometry truss manipulators is proposed,based on fuzzy neural network in this paper. The fuzzy neural network model is used to identify inversedynamic model of redundant variable geometry truss manipulators, and conventional feedback controller isapplied to compensation of external interference and close-loop control. The simulation calculation for a four-celled tetrahedron based variable geometry truss manipulator is give. The method is proved to have goodtrack accuracy and anti-interference characteristics.

  16. 下肢携行外骨骼系统模糊自适应位置控制研究%Research on Fuzzy Adaptive Position Control of Carrying Lower Extreme Exoskeleton

    Institute of Scientific and Technical Information of China (English)

    杨秀霞; 张毅; 归丽华; 杨智勇

    2012-01-01

    In order to overcome the disadvantages of conventional stance phase position control of lower extreme exoskeleton, improve the control precision and the speed, and increase the adaptability and robustness of the control system, a position control algorithm with fixed gravity compensation was presented. Using the characteristics of fuzzy reasoning logic as approaching nonlinear functions, the fuzzy adaptive position control algorithm with fixed gravity compensation was designed, which combines fuzzy control algorithm with the conventional PD control algorithm with fixed gravity compensation. Then the algorithm was applied to position control of lower extreme exoskeleton stance phase. The simulation results show that the algorithm can make the exoskeleton to trace the pilot' s motion rapidly and accurately, as well as decrease the human-machine interaction force, and is robust to the variation of the load, which validates the feasibility of the presented control method.%研究下肢携行外骨骼系统稳定性控制问题.下肢携行外骨骼系统支撑行走阶段位置控制存在稳定性、实时性不足,提高位置控制的速度及精度,使具有适应性和鲁棒性,为了克服具有实时重力补偿的传统方法,提出固定重力补偿的位置控制方法,并利用模糊推理逻辑可逼近非线性函数的特点,将模糊控制算法与具有固定重力补偿的传统PD控制算法相结合,提出了具有固定重力补偿的模糊自适应位置控制算法,应用到下肢携行外骨骼支撑行走阶段的位置控制中.仿真结果表明控制方法能够使外骨骼准确迅速跟踪人体运动,并能够显著减小人所施加的力矩,系统鲁棒性较强,能够适应负载的变化,证明是一种行之有效的控制方法.

  17. H∞ synchronization of uncertain fractional order chaotic systems: adaptive fuzzy approach.

    Science.gov (United States)

    Lin, Tsung-Chih; Kuo, Chia-Hao

    2011-10-01

    This paper presents a novel adaptive fuzzy logic controller (FLC) equipped with an adaptive algorithm to achieve H(∞) synchronization performance for uncertain fractional order chaotic systems. In order to handle the high level of uncertainties and noisy training data, a desired synchronization error can be attenuated to a prescribed level by incorporating fuzzy control design and H(∞) tracking approach. Based on a Lyapunov stability criterion, not only the performance of the proposed method is satisfying with an acceptable synchronization error level, but also a rather simple stability analysis is performed. The simulation results signify the effectiveness of the proposed control scheme. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  18. Performance Evaluation of Road Traffic Control Using a Fuzzy Cellular Model

    CERN Document Server

    Płaczek, Bartłomiej

    2011-01-01

    In this paper a method is proposed for performance evaluation of road traffic control systems. The method is designed to be implemented in an on-line simulation environment, which enables optimisation of adaptive traffic control strategies. Performance measures are computed using a fuzzy cellular traffic model, formulated as a hybrid system combining cellular automata and fuzzy calculus. Experimental results show that the introduced method allows the performance to be evaluated using imprecise traffic measurements. Moreover, the fuzzy definitions of performance measures are convenient for uncertainty determination in traffic control decisions.

  19. Fuzzy multinomial control chart and its application

    Science.gov (United States)

    Wibawati, Mashuri, Muhammad; Purhadi, Irhamah

    2016-03-01

    Control chart is a technique that has been used widely in industry and services. P chart is the simplest control chart. In this chart, item is classified into two categories as either conforming and non conforming. This chart based on binomial distribution. In practice, each item can classify in more than two categories such as very bad, bad, good and very good. Then to monitor the process we used multinomial p control chart. However, if the classification is an element of vagueness, the fuzzy multinomial control chart (FM) is more appropriately used. Control limit of FM chart obtained multinomial distribution and the degree of membership using fuzzy trianguler are 0, 0.25. 0.5 and 1. This chart will be applied to the data glass and will compare with multinomial p control chart.

  20. Design and Implementation of a Fuzzy Controller for Small Rotation Angles

    Directory of Open Access Journals (Sweden)

    Mohammed Mahmood Hussein

    2011-06-01

    Full Text Available This paper present an adaptation mechanism for fuzzy logic controller FLC in order to perfect the response performance against small rotation angles of real D.C. motor with unknown parameters. A supervisor fuzzy controller SFC is designed to continuously adjust, on-line, the universe of discourse UOD of the basic fuzzy controller BFC input variables based on position error and change of position error. Performance of the proposed adaptive fuzzy controller is compared with corresponding conventional FLC in terms of several performance measures such rise time, settling time, peak overshoot, and steady state error. The system design and implementation are carried out using LabVIEW 2009 with NI PCI-6251 data acquisition DAQ card. The practical results demonstrate using self tuning FLC scheme grant a better performance as compared with conventional FLC which is incapable of rotating a motor if the rotation angle is being small.

  1. 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.

  2. Fuzzy Sliding Mode Control of Plate Vibrations

    OpenAIRE

    Manu Sharma; Singh, S. P.

    2010-01-01

    In this paper, fuzzy logic is meshed with sliding mode control, in order to control vibrations of a cantilevered plate. Test plate is instrumented with a piezoelectric sensor patch and a piezoelectric actuator patch. Finite element method is used to obtain mathematical model of the test plate. A design approach of a sliding mode controller for linear systems with mismatched time-varying uncertainties is used in this paper. It is found that chattering around the sliding surface in the sliding ...

  3. Generalizations of fuzzy linguistic control points in geometric design

    Science.gov (United States)

    Sallehuddin, M. H.; Wahab, A. F.; Gobithaasan, R. U.

    2014-07-01

    Control points are geometric primitives that play an important role in designing the geometry curve and surface. When these control points are blended with some basis functions, there are several geometric models such as Bezier, B-spline and NURBS(Non-Uniform Rational B-Spline) will be produced. If the control points are defined by the theory of fuzzy sets, then fuzzy geometric models are produced. But the fuzzy geometric models can only solve the problem of uncertainty complex. This paper proposes a new definition of fuzzy control points with linguistic terms. When the fuzzy control points with linguistic terms are blended with basis functions, then a fuzzy linguistic geometric model is produced. This paper ends with some numerical examples illustrating linguistic control attributes of fuzzy geometric models.

  4. A Crossed Pack-to-Cell Equalizer Based on Quasi-Resonant LC Converter with Adaptive Fuzzy Logic Equalization Control for Series-connected Lithium-Ion Battery Strings

    DEFF Research Database (Denmark)

    Shang, Yunlong; Zhang, Chenghui; Cui, Naxin

    2015-01-01

    The equalization speed, efficiency, and control are the key issues of battery equalization. This paper proposes a crossed pack-to-cell equalizer based on quasi-resonant LC converter (QRLCC). The battery string is divided into M modules, and each module consists of N series-connected cells....... The energy can be transferred directly from a battery module to the lowest voltage cell (LVC) in the next adjacent module, which results in an enhancement of equalization efficiency and current. The QRLCC is employed to gain zero-current switching (ZCS), leading to a reduction of power losses...... and electromagnetic interference (EMI). Furthermore, an adaptive fuzzy logic control (AFLC) algorithm is employed to online regulate the equalization period according to the voltage difference between cells and the cell voltage, not only greatly abbreviating the balancing time but also effectively preventing over...

  5. Robust control for a class of uncertain switched fuzzy systems

    Institute of Scientific and Technical Information of China (English)

    Hong YANG; Jun ZHAO

    2007-01-01

    A model of uncertain switched fuzzy systems whose subsystems are uncertain fuzzy systems is presented.Robust controllers for a class of switched fuzzy systems are designed by using the Lyapunov function method. Stability conditions for global asymptotic stability are developed and a switching strategy is proposed. An example shows the effectiveness of the method.

  6. Adaptive edge image enhancement based on maximum fuzzy entropy

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xiu-hua; YANG Kun-tao

    2006-01-01

    Based on the maximum fuzzy entropy principle,the edge image with low contrast is optimally classified into two classes adaptively,under the condition of probability partition and fuzzy partition.The optimal threshold is used as the classified threshold value,and a local parametric gray-level transformation is applied to the obtained classes.By means of two parameters representing,the homogeneity of the regions in edge image is improved.The excellent performance of the proposed technique is exercisable through simulation results on a set of test images.It is shown how the extracted and enhanced edges provide an efficient edge-representation of images.It is shown that the proposed technique possesses excellent performance in homogeneity through simulations on a set of test images,and the extracted and enhanced edges provide an efficient edge-representation of images.

  7. 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....

  8. Daylight illuminance control with fuzzy logic

    Energy Technology Data Exchange (ETDEWEB)

    Trobec Lah, Mateja; Peternelj, Joze; Krainer, Ales [University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova cesta 2, 1000 Ljubljana (Slovenia); Zupancic, Borut [University of Ljubljana, Faculty of Electrical Engineering, Trzaska 25, 1000 Ljubljana (Slovenia)

    2006-03-15

    The purpose is to take full advantage of daylight for inside illumination. The inside illuminance and luminous efficacy of the available solar radiation were analyzed. The paper deals with the controlled dynamic illuminance response of built environment in real-time conditions. The aim is controlled functioning of the roller blind as a regulation device to assure the desired inside illuminance with smooth roller blind moving. Automatic illuminance control based on fuzzy logic is realized on a test chamber with an opening on the south side. The development and design of the fuzzy controller for the corresponding positioning of the roller blind with the available solar radiation as external disturbance is the subject of this paper. (author)

  9. Neuro-fuzzy predictive control for nonlinear application

    Institute of Scientific and Technical Information of China (English)

    CHEN Dong-xiang; WANG Gang; LV Shi-xia

    2008-01-01

    Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to re-duce the model errors caused by changes of the process under control. To cope with the difficult problem of non-linear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.

  10. Fuzzy Control Method with Application for Functional Neuromuscular Stimulation System

    Institute of Scientific and Technical Information of China (English)

    吴怀宇; 周兆英; 熊沈蜀

    2001-01-01

    A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physicalmultiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controllerwas developed with the fuzzy control rule base. Simulation experiments were then conducted for the joint angletrajectories of both the elbow flexion and the wrist flexion using the proposed fuzzy control algorithm and aconventional PID control algorithm with the FNS physical multiarticular muscle control system. The simulationresults demonstrated that the proposed fuzzy control method is more suitable for the physiologicalcharacteristics than conventional PID control. In particular, both the trajectory-following and the stability of theFNS multiarticular muscle control system were greatly improved. Furthermore, the stimulating pulse trainsgenerated by the fuzzy controller were stable and smooth.``

  11. Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management

    Science.gov (United States)

    Chang, N.

    2006-12-01

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

  12. Design Method for the Magnetic Bearing Control System with Fuzzy-PID Approach

    Institute of Scientific and Technical Information of China (English)

    XU Chun-guang; L(U) Dong-ming; HAO Juan

    2008-01-01

    The five degree freedom magnetic bearing is researched and its structure and working principles are introduced also.Based on the fuzzy control technology,combining fuzzy algorithm and PID control method,identifying the transition process mode of the online system to get the PID parameters'self-adjusting,the magnetic bearing system's Fuzzy-PID nonlinear controller is designed by analyzing the system control demands.The Fuzzy-PID nonlinear controller can deal with the magnetic bearing system's open loop instability and strong nonlinearity,and the approach could improve the system's rapidity,adaptability,stability and dynamic characteristics.Comparative analysis and experiments are conducted between linear PID and nonlinear fuzzyPID control methods,the results show that the fuzzy-PID controller is better,and the five-freedom magnetic bearing's rotary precision experiments are conducted by the fuzzy-PID controller,it satisfies the control rotary precision demands and realizes the bearing's steady floating and rotating.

  13. Optimized and Self-Organized Fuzzy Logic Controller for pH Neutralization Process

    Directory of Open Access Journals (Sweden)

    Parikshit Kishor Singh

    2013-11-01

    Full Text Available To conform to strict environmental safety regulations, pH control is used in many industrial applications. For this purpose modern process industries are increasingly relying on intelligent and adaptive control strategies. On one hand intelligent control strategies try to imitate human way of thinking and decision making using artificial intelligence (AI based techniques such as fuzzy logic whereas on the other hand adaptive mechanism ensures adjusting of the controller parameters. A self-organized fuzzy logic controller (SOFLC is intelligent in nature and adapts its performance to meet the figure of merit. This paper presents an optimized SOFLC for pH control using performance correction table. The fuzzy adaptation mechanism basically involves a penalty for the output membership functions if the controller performance is poor. The evolutionary genetic algorithm (GA is used for optimization of input-output scaling factors of the conventional fuzzy logic controller (FLC as well as elements of the fuzzy performance correction table. The resulting optimized SOFLC is compared with optimized FLC for servo and regulatory control. Comparison indicate superior performance of SOFLC over FLC in terms of much reduced integral of squared error (ISE, maximum overshoot and undershoot, and increased speed of response.

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

    CERN Document Server

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

    2006-01-01

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

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

    Science.gov (United States)

    Mitra, Sunanda; Pemmaraju, Surya

    1992-01-01

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

  16. Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

    Science.gov (United States)

    Lin, Chuan-Kai

    2005-04-01

    A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.

  17. 自适应模糊滑模变结构在永磁同步电动机的应用%Adaptive Fuzzy Sliding mode Control into Permanent Magnet Synchronous Motor

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

      针对永磁同步电动机存在参数时变、多变量耦合、负载扰动以及非线性等不确定因素,提出一种新颖的、带有扩展反馈和切换增益的自适应模糊滑模变结构控制方法。%Against the indefinite factors of the time variation of parameters, multivariable coupling, load distur-bances and nonlinear of the permanent magnet synchronous motor, an innovative approach to adaptive fuzzy sliding mode control with a new type of extended feedback and switching gains was introduced.

  18. Application of Fuzzy Logic in Control of Switched Reluctance Motor

    Directory of Open Access Journals (Sweden)

    Pavel Brandstetter

    2006-01-01

    Full Text Available The flux linkage of switched reluctance motor (SRM depends on the stator current and position between the rotor and stator poles. The fact determines that during control of SRM current with the help of classical PI controllers in a wide regulation range unsatisfied results occur. The main reasons of the mentioned situation are big changes of the stator inductance depending on the stator current and rotor position. In a switched reluctance motor the stator phase inductance is a non-linear function of the stator phase current and rotor position. Fuzzy controller and fuzzy logic are generally non-linear systems; hence they can provide better performance in this case. Fuzzy controller is mostly presented as a direct fuzzy controller or as a system, which realizes continued changing parameters of other controller, so-called fuzzy supervisor. Referring to the usage of fuzzy logic as a supervisor of conventional PI controller in control of SRM possible improvement occurs.

  19. Optimal radial distribution system reconfiguration using fuzzy adaptation of evolutionary programming

    Energy Technology Data Exchange (ETDEWEB)

    Venkatesh, B.; Ranjan, R. [Multimedia Univ. (Melaka Campus). (Malaysia). Faculty of Engineering and Technology

    2003-12-01

    Optimal reconfiguration of Radial Distribution System (RDS) is done under the umbrella of Supervisory Control and Data Acquisition (SCADA) systems to achieve the best voltage profile and minimal kW losses amongst several objectives. This problem requires the determination of the best combination of feeders from each loop in the RDS to be switched out such that the resulting RDS gives the optimal performance in the chosen circumstance. The problem has a discontinuous solution space and certain problem variables assume discrete values of zero or one. This paper proposes a method that uses fuzzy adaptation of Evolutionary Programming (FEP) as a solution technique. FEP technique has been chosen as it is particularly suited while solving optimization problems with discontinuous solution space and when the global optimum is desired. Fuzzy adaptation of EP is necessitated while considering optimization of multiple objectives. The proposed method is tested on established RDS and results are presented. (author)

  20. Maximum entropy approach to fuzzy control

    Science.gov (United States)

    Ramer, Arthur; Kreinovich, Vladik YA.

    1992-01-01

    For the same expert knowledge, if one uses different &- and V-operations in a fuzzy control methodology, one ends up with different control strategies. Each choice of these operations restricts the set of possible control strategies. Since a wrong choice can lead to a low quality control, it is reasonable to try to loose as few possibilities as possible. This idea is formalized and it is shown that it leads to the choice of min(a + b,1) for V and min(a,b) for &. This choice was tried on NASA Shuttle simulator; it leads to a maximally stable control.

  1. Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology

    Science.gov (United States)

    Bonissone, Piero P.

    1995-06-01

    We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.

  2. Implementation of Fuzzy-PID in Smart Car Control

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    <正>An unmanued smart car control system and the fuzzy-PID control algorithm are produced.A design scheme of fuzzy-PID controller is put forward.The simulation analysis from matlab indicated that the dynamic performance of fuzzy-PID control algorithm is better than that of usual PID.Experimental result of smart car show that it can follow the black guid line well and fast-stable complete running the whole trip.

  3. AN INTELLIGENT CONTROL SYSTEM BASED ON RECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

    Institute of Scientific and Technical Information of China (English)

    JIA Li; YU Jinshou

    2005-01-01

    In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information (ey)/(e)u for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM on line. Lastly, in order to evaluate the performance of theproposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.

  4. Fuzzy Logic in Traffic Engineering: A Review on Signal Control

    Directory of Open Access Journals (Sweden)

    Milan Koukol

    2015-01-01

    Full Text Available Since 1965 when the fuzzy logic and fuzzy algebra were introduced by Lotfi Zadeh, the fuzzy theory successfully found its applications in the wide range of subject fields. This is mainly due to its ability to process various data, including vague or uncertain data, and provide results that are suitable for the decision making. This paper aims to provide comprehensive overview of literature on fuzzy control systems used for the management of the road traffic flow at road junctions. Several theoretical approaches from basic fuzzy models from the late 1970s to most recent combinations of real-time data with fuzzy inference system and genetic algorithms are mentioned and discussed throughout the paper. In most cases, fuzzy logic controllers provide considerable improvements in the efficiency of traffic junctions’ management.

  5. Adaptive Quantization Index Modulation Audio Watermarking based on Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Sunita V. Dhavale

    2014-02-01

    Full Text Available Many of the adaptive watermarking schemes reported in the literature consider only local audio signal properties. Many schemes require complex computation along with manual parameter settings. In this paper, we propose a novel, fuzzy, adaptive audio watermarking algorithm based on both global and local audio signal properties. The algorithm performs well for dynamic range of audio signals without requiring manual initial parameter selection. Here, mean value of energy (MVE and variance of spectral flux (VSF of a given audio signal constitutes global components, while the energy of each audio frame acts as local component. The Quantization Index Modulation (QIM step size Δ is made adaptive to both the global and local features. The global component automates the initial selection of Δ using the fuzzy inference system while the local component controls the variation in it based on the energy of individual audio frame. Hence Δ adaptively controls the strength of watermark to meet both the robustness and inaudibility requirements, making the system independent of audio nature. Experimental results reveal that our adaptive scheme outperforms other fixed step sized QIM schemes and adaptive schemes and is highly robust against general attacks.

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

    Directory of Open Access Journals (Sweden)

    Zain Anwar Ali

    2016-05-01

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

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

    Science.gov (United States)

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

    2016-05-09

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

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

    Science.gov (United States)

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

    2016-01-01

    In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. PMID:27171084

  9. Fuzzy Control of Chaotic System with Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang

    2002-01-01

    A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.

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

    Science.gov (United States)

    Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel

    2014-12-01

    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.

  11. Analysis of one dimensional and two dimensional fuzzy controllers

    Institute of Scientific and Technical Information of China (English)

    Ban Xiaojun; Gao Xiaozhi; Huang Xianlin; Wu Tianbao

    2006-01-01

    The analytical structures and the corresponding mathematical properties of the one dimensional and two dimensional fuzzy controllers are first investigated in detail.The nature of these two kinds of fuzzy controllers is next probed from the perspective of control engineering. For the one dimensional fuzzy controller, it is concluded that this controller is a combination of a saturation element and a nonlinear proportional controller, and the system that employs the one dimensional fuzzy controller is the combination of an open-loop control system and a closedloop control system. For the latter case, it is concluded that it is a hybrid controller, which comprises the saturation part, zero-output part, nonlinear derivative part, nonlinear proportional part, as well as nonlinear proportional-derivative part, and the two dimensional fuzzy controller-based control system is a loop-varying system with varying number of control loops.

  12. Fuzzy PID Adaptive Control Algorithm of Pulling-up Mechanism on the Cassava Harvester%木薯收获机块根拔起机构自适应控制算法研究

    Institute of Scientific and Technical Information of China (English)

    郑贤; 陈科余; 杨望; 杨坚; 李杨

    2017-01-01

    The pulling-up cassava harvester can ’ t pulling up the cassava root accurately according to the variation of cassava growth condition and soil condition,and its root pulling off loss rate is very high when the cassava growth condi-tion and soil condition have a big change. To solve the problem,this paper adopts the co_simulation technology and uses fuzzy PID adaptive control algorithm which based on the optimal velocity to control the cassava root lifting velocity accord-ing to the pulling force change,and regards reducing root pulling off loss rate as a goal,it shows that the optimizational fuzzy PID adaptive control algorithm can make the speed of gear rack swing hydraulic cylinder track the given speed curve very well,and the whole machine is very well in the self-adaption and it can adapt to different working load.%针对挖拔式木薯收获机无法根据木薯块根生长情况和土质情况的变化实现精确控制木薯块根拔起,且在木薯块根生长情况和土质情况变化大时其块根拔断损失率高的情况,采用联合仿真技术,以较优块根拔起速度模型为基础,根据拔起力变化,控制木薯收获机拔起速度使其达到减少块根拔断损失率的目标,对木薯块根拔起过程进行模糊PID 自适应控制,且对模糊PID 自适应控制算法进行了优化和物理试验验征。结果表明:在木薯块根拔起过程中,优化的模糊 PID 控制算法能使齿轮齿条摆动液压缸转速很好地跟踪给定转速曲线,且整机的自适应控制效果好,能适应不同的工作载荷。

  13. 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.

  14. Application of Self-adaptive Controller Based on Fuzzy Theory in Direct Torque Control System%基于模糊理论的自适应控制器在直接转矩控制系统中的应用

    Institute of Scientific and Technical Information of China (English)

    曲瀛; 窦日轩

    2001-01-01

    This paper introduces a self-adaptive controller based on fuzzy theory which is used as the speed controller in direct torque control system. The controller is made up of traditional PID and fuzzy inference. The parameters of the traditional PID are adjusted by fuzzy inference in real-time so that it can come to self-adaptive controlling of speed.%采用一种基于模糊理论的自适应控制器作为直接转矩控制系统的速度调节器,该控制器由常规PD控制部分和模糊推理两部分组成,通过模糊推理实现常规PID控制器参数的实时优化调整,从而实现速度环的自适应控制,有效地提高了系统响应速度。

  15. Fuzzy Technique Tracking Control for Multiple Unmanned Ships

    OpenAIRE

    Ramzi Fraga; Liu Sheng

    2013-01-01

    A Fuzzy logic control law is presented and implemented for trajectory tracking of multiple under actuated autonomous surface vessels. In this study, an individual unmanned ship is used to be the leader that tracks the desired path; other unmanned ships are used to be the followers which track the leader only by using its position. A fuzzy controller was implemented for the ship leader position with a constant velocity; however, the ship follower needed a fuzzy controller for the position and ...

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

    Directory of Open Access Journals (Sweden)

    Duka Adrian-Vasile

    2011-12-01

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

  17. Using genetic algorithm based fuzzy adaptive resonance theory for clustering analysis

    Institute of Scientific and Technical Information of China (English)

    LIU Bo; WANG Yong; WANG Hong-jian

    2006-01-01

    In the clustering applications field, fuzzy adaptive resonance theory system has been widely applied. But, three parameters of fuzzy adaptive resonance theory need to be adjusted manually for obtaining better clustering. It needs much time to test and does not assure a best result. Genetic algorithm is an optimal mathematical search technique based on the principles of natural selection and genetic recombination. So, to make the fuzzy adaptive resonance theory parameters choosing process automation, an approach incorporating genetic algorithm and fuzzy adaptive resonance theory neural network has been applied. Then, the best clustering result can be obtained.Through experiment, it can be proved that the most appropriate parameters of fuzzy adaptive resonance theory can be gained effectively by this approach.

  18. An Adaptive Fuzzy Framework based on Optimized Fuzzy Contexts for Detecting Network Intrusions

    Directory of Open Access Journals (Sweden)

    Habib Ullah Baig

    2010-10-01

    Full Text Available Anomaly based Intrusion Detection System (AIDS is one of the key component of a reliable security infrastructure. Working at second line of defense, detection accuracy is the key objective that largely depends upon the precision of its normal profile. Due to existence of vague boundaries between normal and anomalous classes and dynamic network behavior, building accurate and generalize normal profile is very difficult. Based on the assumption that intruder?s behavior can be grouped into different phases active at different times, this article proposes to evolve and use ?short-term fuzzy profiles/contexts? for each such individual intrusion phase resulting in enhanced detection accuracy for low-level attacks. The result is a context-driven, adaptable implementation framework based on a double layer hierarchy of fuzzy sensors. The framework adapts to network conditions by switching between different contexts, according to network traffic patterns, anomaly conditions and organization?s security policies. These contexts are evolved in incremental fashion with genetic algorithm using real-time network traces. The framework is tested using DARPA 98/99 dataset showing accurate detection of low-level DoS attack.

  19. 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

  20. Development of an adaptive online fuzzy arbitrator for forecasting short-term natural gas usage

    Science.gov (United States)

    Lukas, Richard James, Jr.

    2001-07-01

    The focus of the work is on the development and utilization of a self-assembling Fuzzy logic controller for the purpose of improving short term natural gas load forecasts generated by artificial neural networks (ANN) and linear regression (LR) models. The approach is to form a matrix of dynamic post processors (DPP), composed of ARMAX models, which use load estimates generated by ANNs and LRs as inputs. The problem is to then determine the performance of each DPP under different operating conditions, and to generate a final load estimate using a Fuzzy logic controller. The contributions of this research are as follows. First, as part of a residuals analysis, prefiltering and nonlinear transforms are explored for the purpose of increasing the correlation of environmental input factors with gas load, while decreasing multicollinearity. This has the effect of reducing the covariance of model parameters and increasing forecast confidence. The result of this analysis will be used to develop ARMAX models to postfilter the ANN and LR forecast model estimates. The gas operating regions will be characterized by an adaptive clustering algorithm that will partition operating conditions into distinct patterns with unique consumption characteristics. Finally, an adaptive online Fuzzy controller identifies the characteristics of each DPP under different operating conditions, and generates a weighted average of the DPP estimators to produce the final gas load estimate.

  1. STUDY ON FUZZY SELF-LEARNING CONTROL SYSTEM FOR SHIP STEERING

    Institute of Scientific and Technical Information of China (English)

    LIU Qing; WU Xiu-heng; ZOU Zao-jian

    2004-01-01

    Fuzzy control has shown success in some application areas and emerged as an alternative to some conventional control schemes. There are also some drawbacks in this approach, for example it is hard to justify the choice of fuzzy controller parameters and control rules, and control precision is low, and so on. Fuzzy control is developing towards self-learning and adaptive. The ship steering motion is a nonlinear, coupling, time-delay complicated system. How to control it effectively is the problem that many scholars are studying. In this paper, based on the repeated control of the robot, the self-learning arithmetic was worked out. The arithmetic was realized in fuzzy logic way and used in cargo steering. It is the first time for the arithmetic to be used in cargo steering. Our simulation results show that the arithmetic is effective and has several potential advantages over conventional fuzzy control.This work lays a foundation in modeling and analyzing the fuzzy learning control system.

  2. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Chien-Hao Tseng

    2016-07-01

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

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

    Science.gov (United States)

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

    2016-07-26

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

  4. Approach to Fuzzy Model-Free Adaptive Coordinating Control for Distillation Processes%精馏过程的模糊无模型自适应协调控制方法

    Institute of Scientific and Technical Information of China (English)

    苗帝; 刘振娟; 李宏光

    2013-01-01

    Dealing with multivariable coordinating control problems and regarding multi-input multi-output (MI-MO ) ethanol-water distillation processes with unknown process models and strong coupling characteristic, the control requirements of main and deputy controlled parameters are different and PID control method has poor performance. In the presence of immeasurable disturbances, it is imperative to coordinate the related manipulate variables to ensure both main parameters keeping at desired values and the other process parameters running smoothly. In this context, a control approach of fuzzy model-free adaptive coordinating was introduced. Taking the advantage of model-free adaptive control ( MFAC) metrics, deviations and deviation changes of measurements were utilized to accommodate fuzzy rules concerning alternative control performances of main and deputy controlled parameters which can be employed to adaptively update the parameters of corresponding model-free adaptive controllers accordingly. This novel strategy is eligible for solving coordinating control problems of ethanol-water distillation processes, meeting the control requirements of both main and deputy controlled parameters as well as extending the application scope of model-free adaptive control. The case study demonstrates the effectiveness of the contribution.%研究多变量协调控制问题,针对模型未知且具有强耦合特性的多输入多输出乙醇-水精馏过程,主副被控参数的控制要求不同,常规PID控制性能差,当出现显著的不可测干扰时,应协调相关的操纵变量,既保证关键参数不偏离期望值,又使得其它被控过程参数的波动较小.为了解决上述问题,提出了一类模糊无模型自适应协调控制方法.无模型自适应控制结构,通过充分利用过程控制参数的偏差和偏差变化率等信息,建立了主副控制参数不同控制目标的模糊规则,自适应地改变无模型自适应控制器的参数.该方法

  5. On Controllability and Observability of Fuzzy Dynamical Matrix Lyapunov Systems

    Directory of Open Access Journals (Sweden)

    M. S. N. Murty

    2008-04-01

    Full Text Available We provide a way to combine matrix Lyapunov systems with fuzzy rules to form a new fuzzy system called fuzzy dynamical matrix Lyapunov system, which can be regarded as a new approach to intelligent control. First, we study the controllability property of the fuzzy dynamical matrix Lyapunov system and provide a sufficient condition for its controllability with the use of fuzzy rule base. The significance of our result is that given a deterministic system and a fuzzy state with rule base, we can determine the rule base for the control. Further, we discuss the concept of observability and give a sufficient condition for the system to be observable. The advantage of our result is that we can determine the rule base for the initial value without solving the system.

  6. 风帆助航船舶运动的模糊自适应迭代滑模控制%Fuzzy-adaptive iterative sliding-mode control for sail-assisted ship motion

    Institute of Scientific and Technical Information of China (English)

    沈智鹏; 姜仲昊; 王国峰; 郭晨

    2016-01-01

    A kind of fuzzy⁃adaptive nonlinear sliding⁃mode controller is presented for a sail⁃assisted ship motion mod⁃el, which has the characteristics of high nonlinearity and uncertainty. The iterative sliding⁃mode controller ( ISMC) , which uses a nonlinear hyperbolic tangent function, is designed for the system output. The sliding⁃surface feedback control method is combined with the ISMC without needing to estimate the uncertain parameters and disturbances. The stability of the proposed controller can be proved by the strict boundedness of the hyperbolic tangent function and the constraint of the system input. To enhance the adaptability of the controller, a fuzzy system is introduced to optimize the parameters of the ISMC. Finally, numerical simulations were carried out of the 76000 DWT large ocean⁃going bulk carrier ‘Wen Zhuhai’ . The results of the simulations indicate that the proposed fuzzy nonlinear iterative sliding⁃mode controller ( FAISMC) is robust against perturbations from the uncertain parameters and wave disturbances, and its control output is more appropriate than that from the ISMC.%针对风帆助航船舶运动模型的不确定性和高度非线性特点,设计了一种自适应非线性滑模控制器。该控制器利用非线性双曲正切函数对系统输出进行迭代滑动模态设计,应用滑模面反馈控制方法,无需对系统的不确定项和外界干扰进行估计,根据双曲正切函数的严格有界性和控制输入约束条件证明了控制器稳定性,同时引入模糊系统对迭代滑模参数进行优化,增强控制器的自适应性。以“文竹海”号76000DWT 散货船为目标进行控制仿真,结果表明,所设计控制器对系统模型不确定参数摄动及风浪作用不敏感,具有强鲁棒性,且与迭代滑模控制器相比所得控制量输出更加合理有效。

  7. Stochastic Global Optimization and Its Applications with Fuzzy Adaptive Simulated Annealing

    CERN Document Server

    Aguiar e Oliveira Junior, Hime; Petraglia, Antonio; Rembold Petraglia, Mariane; Augusta Soares Machado, Maria

    2012-01-01

    Stochastic global optimization is a very important subject, that has applications in virtually all areas of science and technology. Therefore there is nothing more opportune than writing a book about a successful and mature algorithm that turned out to be a good tool in solving difficult problems. Here we present some techniques for solving  several problems by means of Fuzzy Adaptive Simulated Annealing (Fuzzy ASA), a fuzzy-controlled version of ASA, and by ASA itself. ASA is a sophisticated global optimization algorithm that is based upon ideas of the simulated annealing paradigm, coded in the C programming language and developed to statistically find the best global fit of a nonlinear constrained, non-convex cost function over a multi-dimensional space. By presenting detailed examples of its application we want to stimulate the reader’s intuition and make the use of Fuzzy ASA (or regular ASA) easier for everyone wishing to use these tools to solve problems. We kept formal mathematical requirements to a...

  8. Performance Comparison of Conventional Controller with Fuzzy Logic Controller using Chopper Circuit and Fuzzy Tuned PID Controller

    Directory of Open Access Journals (Sweden)

    Mohammed Shoeb Mohiuddin

    2014-09-01

    Full Text Available It is often difficult to develop an accurate mathematical model of DC motor due to unknown load variation, unknown and unavoidable parameter variations or nonlinearities due to saturation temperature variations and system disturbances. Fuzzy logic application can handle such nonlinearities so that the controller design is fundamentally robust which is not possible in conventional controllers. The knowledge base of a fuzzy logic controller (FLC encapsulates expert knowledge and consists of the Data base (membership functions and Rule-Base of the controller. Optimization of both these knowledge base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables however for greater numbers of inputs, more formal methods of knowledge base optimization are required. In this work, we study the challenging task of controlling the speed of DC motor. The feasibility of such controller design is evaluated by simulation in the MATLAB/Simulink environment. In this study Conventional Proportional Integral Derivative controller, Fuzzy logic controller using a chopper circuit and Fuzzy tuned PID controller are analyzed and compared. Simulation software like MATLAB with Simulink has been used for modeling and simulation purpose. The performance comparison of conventional controller with Fuzzy logic controller using chopper circuit and Fuzzy tuned PID controller has been done in terms of several performance measures Such as Settling time, Rise time and Overshoot.

  9. Fuzzy control for closed-loop, patient-specific hypnosis in intraoperative patients: a simulation study.

    Science.gov (United States)

    Moore, Brett L; Pyeatt, Larry D; Doufas, Anthony G

    2009-01-01

    Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of fuzzy control in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the fuzzy controller demonstrated clinically acceptable performance, suggesting that further study is warranted.

  10. Active structural control by fuzzy logic rules: An introduction

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Y.

    1995-07-01

    An introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single-degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.

  11. Active structural control by fuzzy logic rules: An introduction

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Yu [Argonne National Lab., IL (United States). Reactor Engineering Div.; Wu, Kung C. [Texas Univ., El Paso, TX (United States). Dept. of Mechanical and Industrial Engineering

    1996-12-31

    A zeroth level introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single- degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.

  12. Fuzzy Technique Tracking Control for Multiple Unmanned Ships

    Directory of Open Access Journals (Sweden)

    Ramzi Fraga

    2013-01-01

    Full Text Available A Fuzzy logic control law is presented and implemented for trajectory tracking of multiple under actuated autonomous surface vessels. In this study, an individual unmanned ship is used to be the leader that tracks the desired path; other unmanned ships are used to be the followers which track the leader only by using its position. A fuzzy controller was implemented for the ship leader position with a constant velocity; however, the ship follower needed a fuzzy controller for the position and the forward velocity. Simulation results show that the fuzzy method presents an interesting robustness against the environmental disturbances and effective tracking results.

  13. Iterative Feedback Tuning in Fuzzy Control Systems. Theory and Applications

    Directory of Open Access Journals (Sweden)

    Stefan Preitl

    2006-07-01

    Full Text Available The paper deals with both theoretical and application aspects concerningIterative Feedback Tuning (IFT algorithms in the design of a class of fuzzy controlsystems employing Mamdani-type PI-fuzzy controllers. The presentation is focused on twodegree-of-freedom fuzzy control system structures resulting in one design method. Thestability analysis approach based on Popov’s hyperstability theory solves the convergenceproblems associated to IFT algorithms. The suggested design method is validated by realtimeexperimental results for a fuzzy controlled nonlinear DC drive-type laboratoryequipment.

  14. Fuzzy control of electro-mechanical gearbox actuator

    Institute of Scientific and Technical Information of China (English)

    G Iordanidis; P H Mellor; D Holliday; P M Churn

    2003-01-01

    In this paper, a prototype direct-drive electro-mechanical actuator is proposed to select gears in a high performance gearbox. Because of the nonlinear behavior of the actuator, a fuzzy logic controller is adopted. The result of simulation has proved that the dynamic response obtained using the fuzzy controller is much faster than that obtained using traditional PD controller.

  15. 基于气动人工肌肉的自适应模糊小脑模型神经网络位置跟踪控制%Adaptive Fuzzy CMAC Position Tracking Control Based on Pneumatic Artificial Muscle

    Institute of Scientific and Technical Information of China (English)

    沈伟; 施光林

    2012-01-01

    A single degree freedom pneumatic artificial muscle spring mass system was built. An adaptive fuzzy CMAC (AFCMAC) was setup to track and control the pneumatic artificial muscle system. With the parallel supervision of discrete anti saturation PID (DA_SPID) there is few too big tracking error and pres- sure fluctuation in the beginning of control process, which makes the online real time self adjustment of AFCMAC possible. The online real time self adjustment ability improves the control performance of the adaptive fuzzy CMAC, and finally the spring mass system is entirely controlled by AFCMAC. In the end, the comparative study of control performance between DASPID and CMAC was carried out. The experi- mental results suggest that in nonlinear condition, AFCMAC could get better tracking performance than DASPID.%针对一种气动人工肌肉驱动的弹簧质量位置控制系统,设计了一种自适应模糊小脑模型神经网络(AFCMAC)控制器.离散抗饱和PID(DASPID)并行监督控制设计保证了控制运行初期不会出现较大的跟踪误差和气压波动,使AFCMAC的在线实时学习调整成为可能.在线实时的自适应算法逐步提高了AFCMAC的控制性能,从而最终完全过渡到AFCMAC控制.通过规划AF—CMAC的输入空间,保证了AFCMAC对迟滞力和气压波动等不确定因素的感知能力,为实现AF—CMAC控制奠定了基础.对DASPID与AFCMAC控制器的位置跟踪控制性能进行了对比实验.结果表明,在非线性系统条件下,AFCMAC较之DASPID有着更好的跟踪控制性能和较低的实现难度.

  16. A Literature Review on the Fuzzy Control Chart; Classifications & Analysis

    Directory of Open Access Journals (Sweden)

    Mohammad Hossein Zavvar Sabegh

    2014-08-01

    Full Text Available Quality control plays an important role in increasing the product quality. Fuzzy control charts are more sensitive than Shewhart control chart. Hence, the correct use of fuzzy control chart leads to producing better-quality products. This area is complex because it involves a large scope of industries, and information is not well organized. In this research, we provide a literature review of the control chart under a fuzzy environment with proposing several classifications and analysis. Moreover, our research considered both attribute and variable control chart by analyzing the related researches based on the content analysis method, to classify past and current developments in the fuzzy control chart. This work has included a distribution of articles according to the journal, the case studies related to fuzzy control chart, the percentage of types of fuzzy control charts used in the literature, performance evaluation of the fuzzy control chart and summary of key points of each review paper. Finally, this paper discusses some future research direction and our overviews. The results of this study can help researchers become familiar with well-known journals, fuzzy control charts used in sample case studies, and to extract key points of each paper in minimum time.

  17. Fuzzy PID Control Method for Internet-based Tele-operation Manipulators System

    Directory of Open Access Journals (Sweden)

    Wei Gao

    2013-11-01

    Full Text Available Trajectory tracking control problem for internet-based tele-operation system is researched in this paper. The control structure of master and slave tele-operation manipulators adapts bilateral servo control architecture with force deviation feedback. The simulation model of three degrees of freedom (3-DOF manipulator is presented. In order to ensure the synchronization of positions of the master and slave manipulators, a fuzzy PID control method is proposed. This control algorithm is to adjust the three parameters of PID controller online by fuzzy control method. The contrast simulation experiments of PID and fuzzy PID control methods show that the proposed control method can effectively improve the force and position tracking performance and reduce time delay.

  18. Adaptive inferential sensors based on evolving fuzzy models.

    Science.gov (United States)

    Angelov, Plamen; Kordon, Arthur

    2010-04-01

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

  19. Two-level tuning of fuzzy PID controllers.

    Science.gov (United States)

    Mann, G I; Hu, B G; Gosine, R G

    2001-01-01

    Fuzzy PID tuning requires two stages of tuning; low level tuning followed by high level tuning. At the higher level, a nonlinear tuning is performed to determine the nonlinear characteristics of the fuzzy output. At the lower level, a linear tuning is performed to determine the linear characteristics of the fuzzy output for achieving overall performance of fuzzy control. First, different fuzzy systems are defined and then simplified for two-point control. Non-linearity tuning diagrams are constructed for fuzzy systems in order to perform high level tuning. The linear tuning parameters are deduced from the conventional PID tuning knowledge. Using the tuning diagrams, high level tuning heuristics are developed. Finally, different applications are demonstrated to show the validity of the proposed tuning method.

  20. ISS-based robust adaptive fuzzy algorithm for maintaining a ship's track

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper focuses on the problem of linear track keeping for marine surface vessels.The influence exerted by sea currents on the kinematic equation of ships is considered first.The input-to-state stability (ISS) theory used to verify the system is input-to-state stable.Combining the Nussbaum gain with backstepping techniques, a robust adaptive fuzzy algorithm is presented by employing fuzzy systems as an approximator for unknown nonlinearities in the system.It is proved that the proposed algorithm that guarantees all signals in the closed-loop system are ultimately bounded.Consequently, a ship's linear track-keeping control can be implemented.Simulation results using Dalian Maritime University's ocean-going training ship 'YULONG' are presented to validate the effectiveness of the proposed algorithm.

  1. Train velocity estimation method based on an adaptive filter with fuzzy logic

    Science.gov (United States)

    Pichlík, Petr; Zděnek, Jiří

    2017-03-01

    The train velocity is difficult to determine when the velocity is measured only on the driven or braked locomotive wheelsets. In this case, the calculated train velocity is different from the actual train velocity due to slip velocity or skid velocity respectively. The train velocity is needed for a locomotive controller proper work. For this purpose, an adaptive filter that is tuned by a fuzzy logic is designed and described in the paper. The filter calculates the train longitudinal velocity based on locomotive wheelset velocity. The fuzzy logic is used for the tuning of the filter according to actual wheelset acceleration and wheelset jerk. The simulation results are based on real measured data on a freight train. The results show that the calculated velocity corresponds to the actual train velocity.

  2. Modelling and control PEMFC using fuzzy neural networks

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Proton exchange membrane generation technology is highly efficient, clean and considered as the most hopeful "green" power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online. This paper first simply analyzes the characters of the PEMFC; and then uses the approach and self-study ability of artificial neural networks to build the model of the nonlinear system, and uses the adaptive neural-networks fuzzy infer system (ANFIS) to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusts the model parameters to control it online. The model and control are implemented in SIMULINK environment. Simulation results showed that the test data and model agreed well, so it will be very useful for optimal and real-time control of PEMFC system.

  3. Nonlinear Modeling and Neuro-Fuzzy Control of PEMFC

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The proton exchange membrane generation technology is highly efficient, and clean and is considered as the most hopeful "green" power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online.This paper analyzed the characters of the PEMFC; and used the approach and self-study ability of artificial neural networks to build the model of nonlinear system, and adopted the adaptive neural-networks fuzzy infer system to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusted the model parameters to control online. The model and control were implemented in SIMULINK environment.The results of simulation show the test data and model have a good agreement. The model is useful for the optimal and real time control of PEMFC system.

  4. Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images.

    Science.gov (United States)

    Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin

    2016-10-27

    Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.

  5. Fuzzy Algorithm for Supervisory Voltage/Frequency Control of a Self Excited Induction Generator

    Directory of Open Access Journals (Sweden)

    Hussein F. Soliman

    2006-01-01

    Full Text Available This paper presents the application of a Fuzzy Logic Controller (FLC to regulate the voltage of a Self Excited Induction Generator (SEIG driven by Wind Energy Conversion Schemes (WECS. The proposed FLC is used to tune the integral gain (KI of a Proportional plus Integral (PI controller. Two types of controls, for the generator and for the wind turbine, using a FLC algorithm, are introduced in this paper. The voltage control is performed to adapt the terminal voltage via self excitation. The frequency control is conducted to adjust the stator frequency through tuning the pitch angle of the WECS blades. Both controllers utilize the Fuzzy technique to enhance the overall dynamic performance.  The simulation result depicts a better dynamic response for the system under study during the starting period, and the load variation. The percentage overshoot, rising time and oscillation are better with the fuzzy controller than with the PI controller type. 

  6. On the quasi-controllability of continuous-time dynamic fuzzy control systems

    Energy Technology Data Exchange (ETDEWEB)

    Feng Yuhu [Department of Applied Mathematics, Dong Hua University, Shanghai 200051 (China)]. E-mail: yhfeng@dhu.edu.cn; Hu Liangjian [Department of Applied Mathematics, Dong Hua University, Shanghai 200051 (China)

    2006-10-15

    This paper gives the controllability analysis of continuous-time dynamic fuzzy control system from the aspect of fuzzy differential equations. The fuzzy state is different from the crisp state, as the counterpart of the controllability concept in the classical control theory, the controllable target state must be restricted within some limits. Hence, the concepts of admissible controllable state subset and quasi-controllability are introduced to describe the controllability property for fuzzy control system. The sufficient and necessary conditions for the fuzzy control system to be quasi-controllable are obtained and some examples are given to demonstrate the problems discussed in this paper.

  7. Type-2 Fuzzy Logic in Intelligent Control Applications

    CERN Document Server

    Castillo, Oscar

    2012-01-01

    We describe in this book, hybrid intelligent systems based mainly on type-2 fuzzy logic for intelligent control. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, and bio-inspired optimization algorithms, which can be used to produce powerful automatic control systems. The book is organized in three main parts, which contain a group of chapters around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which can be the basis for achieving intelligent control with interval type-2 fuzzy logic. The second part of the book is comprised of chapters with the main theme of evolutionary optimization of type-2 fuzzy systems in intelligent control with the aim of designing optimal type-2 fuzzy controllers for complex control problems in diverse areas of application, including mobile robotics, aircraft dynamics systems and hardware implementations. Th...

  8. Design of the Fuzzy Control Systems Based on Genetic Algorithm for Intelligent Robots

    Directory of Open Access Journals (Sweden)

    Gyula Mester

    2014-07-01

    Full Text Available This paper gives the structure optimization of fuzzy control systems based on genetic algorithm in the MATLAB environment. The genetic algorithm is a powerful tool for structure optimization of the fuzzy controllers, therefore, in this paper, integration and synthesis of fuzzy logic and genetic algorithm has been proposed. The genetic algorithms are applied for fuzzy rules set, scaling factors and membership functions optimization. The fuzzy control structure initial consist of the 3 membership functions and 9 rules and after the optimization it is enough for the 4 DOF SCARA Robot control to compensate for structured and unstructured uncertainty. Fuzzy controller with the generalized bell membership functions can provide better dynamic performance of the robot then with the triangular membership functions. The proposed joint-space controller is computationally simple and had adaptability to a sudden change in the dynamics of the robot. Results of the computer simulation applied to the 4 DOF SCARA Robot show the validity of the proposed method.

  9. Adaptive fuzzy system for analysis of natural circulation; Sistema fuzzy adaptativo para analise de circulacao natural

    Energy Technology Data Exchange (ETDEWEB)

    Guimaraes, Antonio Cesar Ferreira [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)

    2002-04-01

    This work consists of the analysis of natural circulation in a thermal hydraulics loop to a system of passive cooling of a nuclear reactor. The loop in reduced scale is similar to a passive heat removal system of a Pressurized Water Reactor. Using some experts of the area and of the system simulator, a set of fuzzy rules are defined to represent the problem and the associated uncertainties. The results are satisfactory if compared for example to experimental ones. With this model, inferences can be accomplished by the engineer, for adjustment and control of the problem variables. (author)

  10. Fuzzy control of power converters based on quasilinear modelling

    Science.gov (United States)

    Li, C. K.; Lee, W. L.; Chou, Y. W.

    1995-03-01

    Unlike feedback control by the fuzzy PID method, a new fuzzy control algorithm based on quasilinear modelling of the DC-DC converter is proposed. Investigation is carried out using a buck-boost converter. Simulation results demonstrated that the converter can be regulated with improved performance even when subjected to input disturbance and load variation.

  11. Fuzzy Simulation Human Intelligent Control System Design on Gyratory Breaker

    Institute of Scientific and Technical Information of China (English)

    Wen,Ruchun; Zhao,Shuling; Zhu,Jianwu; Wang,Xiaoyan

    2005-01-01

    In order to deal with the complex process that incurs serious time delay, enormous inertia and nonlinear problems,fuzzy simulation human intelligent control algorithm rules are established. The fuzzy simulation human intelligent controller and the hardware with the single-chip microcomputer are designed and the anti-interference measures to the whole system are provided.

  12. The application research on fuzzy adaptive control algorithm for seeker stabilization loop%模糊自适应控制算法在导引头稳定回路控制中的应用研究

    Institute of Scientific and Technical Information of China (English)

    孟飞; 刘俊; 胡发兴

    2012-01-01

    Seeker technology as one of the core technologies of precision guidance technology, used to complete the search, identification of target tracking and stability. Therefore, a reliable, external disturbance isolation high seeker of stability control system is very important. This article to a seeker stabilization loop for example analysis, analysis of stabilization loop of major works, analysis and modeling, based on the analysis of advantages and disadvantages of traditional algorithms. Fuzzy Adaptive PID control algorithm to improve the traditional algorithms, and new algorithms for analysis. Turned out fuzzy adaptive PID control algorithm is used to regulate the time shorter, smaller overshoot the system, robust performance and advantages of new more powerful anti-disturbance.%导引头技术为精确制导技术的核心技术之一,用来完成对目标的搜索、识别与稳定跟踪。因此,一个工作可靠、对外界扰动隔离能力高的导引头稳定控制系统显得尤为重要。本文以某型导引头稳定回路为例进行分析,分析稳定控制回路的主要工作原理,并对其进行建模分析,在此基础上对传统PID控制算法的优缺点进行分析。采用模糊自适应PID控制算法对传统算法进行改进,并对新算法进行仿真分析。结果证明采用模糊自适应PID控制算法具有调节时间更短、系统超调更小、鲁棒性能和抗扰性能更强的优点。

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

    Energy Technology Data Exchange (ETDEWEB)

    Vasconcelos, Wagner Eustaquio de; Lira, Carlos Alberto Brayner de Oliveira; Brito, Thiago Souza Pereira de; Afonso, Antonio Claudio Marques, E-mail: wagner@unicap.br, E-mail: cabol@ufpe.br, E-mail: afonsofisica@gmail.com, E-mail: thiago.brito86@yahoo.com.br [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Centro de Tecnologia e Geociencias. Departamento de Energia Nuclear; Cruz Filho, Antonio Jose da; Marques, Jose Antonio, E-mail: antonio.jscf@gmail.com, E-mail: jamarkss@uol.com.br [Universidade Catolica de Pernambuco (CCT/PUC-PE), Recife, PE (Brazil). Centro de Ciencias e Tecnologia; Teixeira, Marcello Goulart, E-mail: marcellogt@dcc.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil). Instituto de Matematica. Dept. de Matematica

    2013-07-01

    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)

  14. Fuzzy Logic Controller based on geothermal recirculating aquaculture system

    Directory of Open Access Journals (Sweden)

    Hanaa M. Farghally

    2014-01-01

    Full Text Available One of the most common uses of geothermal heat is in recirculation aquaculture systems (RAS where the water temperature is accurately controlled for optimum growing conditions for sustainable and intensive rearing of marine and freshwater fish. This paper presents a design for RAS rearing tank and brazed heat exchanger to be used with geothermal energy as a source of heating water. The heat losses from the RAS tank are calculated using Geo Heat Center Software. Then a plate type heat exchanger is designed using the epsilon – NTU analysis method. For optimal growth and abundance of production, a Fuzzy Logic control (FLC system is applied to control the water temperature (29 °C. A FLC system has several advantages over conventional techniques; relatively simple, fast, adaptive, and its response is better and faster at all atmospheric conditions. Finally, the total system is built in MATLAB/SIMULINK to study the overall performance of control unit.

  15. 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.

  16. Fuzzy Sliding Mode Controller Design Using Takagi-Sugeno Modelled Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    S. Bououden

    2013-01-01

    Full Text Available Adaptive fuzzy sliding mode controller for a class of uncertain nonlinear systems is proposed in this paper. The unknown system dynamics and upper bounds of the minimum approximation errors are adaptively updated with stabilizing adaptive laws. The closed-loop system driven by the proposed controllers is shown to be stable with all the adaptation parameters being bounded. The performance and stability of the proposed control system are achieved analytically using the Lyapunov stability theory. Simulations show that the proposed controller performs well and exhibits good performance.

  17. A fuzzy logic controller for an autonomous mobile robot

    Science.gov (United States)

    Yen, John; Pfluger, Nathan

    1993-01-01

    The ability of a mobile robot system to plan and move intelligently in a dynamic system is needed if robots are to be useful in areas other than controlled environments. An example of a use for this system is to control an autonomous mobile robot in a space station, or other isolated area where it is hard or impossible for human life to exist for long periods of time (e.g., Mars). The system would allow the robot to be programmed to carry out the duties normally accomplished by a human being. Some of the duties that could be accomplished include operating instruments, transporting objects, and maintenance of the environment. The main focus of our early work has been on developing a fuzzy controller that takes a path and adapts it to a given environment. The robot only uses information gathered from the sensors, but retains the ability to avoid dynamically placed obstacles near and along the path. Our fuzzy logic controller is based on the following algorithm: (1) determine the desired direction of travel; (2) determine the allowed direction of travel; and (3) combine the desired and allowed directions in order to determine a direciton that is both desired and allowed. The desired direction of travel is determined by projecting ahead to a point along the path that is closer to the goal. This gives a local direction of travel for the robot and helps to avoid obstacles.

  18. Fuzzy Control of Structural Vibration for Offshore Platforms

    Institute of Scientific and Technical Information of China (English)

    ZHOUYa-jun; ZHAODe-you

    2004-01-01

    During the past three decades, fuzzy logic feedback control systems have been utilized for the suppression of structural vibration in numerous studies. With the main advantages of the fuzzy controller, the inherent robustness and ability to handle nonlinearity, uncertainty and imprecision of the structure, active structural control of offshore platforms is accomplished. The robustness of the controller has been demonstrated through the uncertainty in damping ratios of the platforms. The study suggests that the proposed fuzzy control algorithm of structural vibration for offshore platforms is effective and feasible,thus improving both serviceability and survival. This present method undoubtedly provides an efficient way of the active control for offshore platforms.

  19. 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.

  20. Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization

    OpenAIRE

    Boumediene ALLAOUA; Laoufi, Abdellah; Brahim GASBAOUI; Nasri, Abdelfatah; Abdessalam ABDERRAHMANI

    2008-01-01

    In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became ve...