Showing papers on "Fuzzy control system published in 2003"
TL;DR: To take full advantage of a fuzzy Lyapunov function, a new parallel distributed compensation (PDC) scheme is proposed that feedbacks the time derivatives of premise membership functions.
Abstract: This paper addresses stability analysis and stabilization for Takagi-Sugeno fuzzy systems via a so-called fuzzy Lyapunov function which is a multiple Lyapunov function. The fuzzy Lyapunov function is defined by fuzzily blending quadratic Lyapunov functions. Based on the fuzzy Lyapunov function approach, we give stability conditions for open-loop fuzzy systems and stabilization conditions for closed-loop fuzzy systems. To take full advantage of a fuzzy Lyapunov function, we propose a new parallel distributed compensation (PDC) scheme that feedbacks the time derivatives of premise membership functions. The new PDC contains the ordinary PDC as a special case. A design example illustrates the utility of the fuzzy Lyapunov function approach and the new PDC stabilization method.
923 citations
TL;DR: It is shown that the regulators, the fuzzy observers and the H"~ controller designs based on new observers for the T-S fuzzy systems are very practical and efficient.
664 citations
TL;DR: In this paper, a feedforward MP-point tracking scheme is developed for the coupled-inductor interleaved-boost-converter-fed PV system using a fuzzy controller that results in better tracking performance.
Abstract: The photovoltaic (PV) generator exhibits a nonlinear V-I characteristic and its maximum power (MP) point varies with solar insolation. In this paper, a feedforward MP-point tracking scheme is developed for the coupled-inductor interleaved-boost-converter-fed PV system using a fuzzy controller. The proposed converter has lower switch current stress and improved efficiency over the noncoupled converter system. For a given solar insolation, the tracking algorithm changes the duty ratio of the converter such that the solar cell array voltage equals the voltage corresponding to the MP point. This is done by the feedforward loop, which generates an error signal by comparing the instantaneous array voltage and reference voltage corresponding to the MP point. Depending on the error and change of error signals, the fuzzy controller generates a control signal for the pulsewidth-modulation generator which in turn adjusts the duty ratio of the converter. The reference voltage corresponding to the MP point for the feedforward loop is obtained by an offline trained neural network. Experimental data are used for offline training of the neural network, which employs a backpropagation algorithm. The proposed peak power tracking effectiveness is demonstrated through simulation and experimental results. Tracking performance of the proposed controller is also compared with the conventional proportional-plus-integral-controller-based system. These studies reveal that the fuzzy controller results in better tracking performance.
477 citations
TL;DR: The proposed fuzzy risk analysis method is more flexible and more intelligent than the existing methods due to the fact that it considers the degrees of confidence of decisionmakers' opinions.
Abstract: In this paper, we present a new method for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. Firstly, we present a method called the simple center of gravity method (SCGM) to calculate the center-of-gravity (COG) points of generalized fuzzy numbers. Then, we use the SCGM to propose a new method to measure the degree of similarity between generalized fuzzy numbers. The proposed similarity measure uses the SCGM to calculate the COG points of trapezoidal or triangular generalized fuzzy numbers and then to calculate the degree of similarity between generalized fuzzy numbers. We also prove some properties of the proposed similarity measure and use an example to compare the proposed method with the existing similarity measures. The proposed similarity measure can overcome the drawbacks of the existing methods. We also apply the proposed similarity measure to develop a new method to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis method is more flexible and more intelligent than the existing methods due to the fact that it considers the degrees of confidence of decisionmakers' opinions.
418 citations
TL;DR: A theoretical analysis shows that the proposed method provides better or at least the same results of the methods presented in the literature, and the proposed design method is applied in the control of an inverted pendulum.
Abstract: Relaxed conditions for stability of nonlinear, continuous and discrete-time systems given by fuzzy models are presented. A theoretical analysis shows that the proposed methods provide better or at least the same results of the methods presented in the literature. Numerical results exemplify this fact. These results are also used for fuzzy regulators and observers designs. The nonlinear systems are represented by fuzzy models proposed by Takagi and Sugeno (1985). The stability analysis and the design of controllers are described by linear matrix inequalities, that can be solved efficiently using convex programming techniques. The specification of the decay rate, constrains on control input and output are also discussed.
359 citations
TL;DR: It is proved that uniformly asymptotic output feedback stabilization can be achieved with the tracking error approaching to zero.
Abstract: A stable adaptive fuzzy sliding-mode controller is developed for nonlinear multivariable systems with unavailable states. When the system states are not available, the estimated states from a semi-high gain observer are used to construct the output feedback fuzzy controller by incorporating the dynamic sliding mode. It is proved that uniformly asymptotic output feedback stabilization can be achieved with the tracking error approaching to zero. A nonlinear system simulation example is presented to verify the effectiveness of the proposed controller.
355 citations
TL;DR: A technique for designing an H-infinity fuzzy output feedback control law which guarantees the L2 gain from an exogenous input to a regulated output is less or equal to a prescribed value is developed.
Abstract: Addresses the problem of stabilizing a class of nonlinear systems by using an H/sub /spl infin// fuzzy output feedback controller First, a class of nonlinear systems is approximated by a Takagi-Sugeno (TS) fuzzy model Then, based on a well-known Lyapunov functional approach, we develop a technique for designing an H/sub /spl infin// fuzzy output feedback control law which guarantees the L/sub 2/ gain from an exogenous input to a regulated output is less or equal to a prescribed value A design algorithm for constructing an H/sub /spl infin// fuzzy output feedback controller is given In contrast to the existing results, the premise variables of the H/sub /spl infin// fuzzy output feedback controller are not necessarily to be the same as the premise variables of the TS fuzzy model of the plant A numerical simulation example is presented to illustrate the theory development
331 citations
TL;DR: The TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation and arrives at a method for designing state feedback gain that maximizes the domain of attraction.
Abstract: Takagi-Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation. The TS fuzzy representation of a nonlinear system subject to actuator saturation is presented. In our TS fuzzy representation, the modeling error is also captured by norm-bounded uncertainties. A set invariance condition for the system in the TS fuzzy representation is first established. Based on this set invariance condition, the problem of estimating the domain of attraction of a TS fuzzy system under a constant state feedback law is formulated and solved as a linear matrix inequality (LMI) optimization problem. By viewing the state feedback gain as an extra free parameter in the LMI optimization problem, we arrive at a method for designing state feedback gain that maximizes the domain of attraction. A fuzzy scheduling control design method is also introduced to further enlarge the domain of attraction. An inverted pendulum is used to show the effectiveness of the proposed fuzzy controller.
321 citations
TL;DR: The simulation results demonstrate that the proposed hybrid fuzzy control system can guarantee the system stability and also maintain a good tracking performance.
Abstract: A hybrid indirect and direct adaptive fuzzy output tracking control schemes are developed for a class of nonlinear multiple-input-multiple-output (MIMO) systems. This hybrid control system consists of observer and other different control components. Using the state observer, it does not require the system states to be available for measurement. Assisted by observer-based state feedback control component, the adaptive fuzzy system plays a dominant role to maintain the closed-loop stability. Being the auxiliary compensation, H/sup /spl infin// control and sliding mode control are designed to suppress the influence of external disturbance and remove fuzzy approximation error, respectively. Thus, the system performance can be greatly improved. The simulation results demonstrate that the proposed hybrid fuzzy control system can guarantee the system stability and also maintain a good tracking performance.
282 citations
TL;DR: The problems and methods of control of chaos, which in the last decade was the subject of intensive studies, were reviewed and the basic results obtained within the framework of the traditional linear, nonlinear, and adaptive control, as well as the neural network systems and fuzzy systems were presented.
Abstract: The problems and methods of control of chaos, which in the last decade was the subject of intensive studies, were reviewed. The three historically earliest and most actively developing directions of research such as the open-loop control based on periodic system excitation, the method of Poincare map linearization (OGY method), and the method of time-delayed feedback (Pyragas method) were discussed in detail. The basic results obtained within the framework of the traditional linear, nonlinear, and adaptive control, as well as the neural network systems and fuzzy systems were presented. The open problems concerned mostly with support of the methods were formulated. The second part of the review will be devoted to the most interesting applications.
261 citations
TL;DR: A new quadratic stability condition, more simple than that in a previous paper, has been proposed and two new sufficient conditions in the terms of linear matrix inequalities (LMIs) which guarantee the existence of the state feedback H/sub /spl infin// control for the T-S fuzzy systems have been proposed.
Abstract: In this paper, the problems of quadratic stability conditions and H/sub /spl infin// control designs for Takagi-Sugeno (T-S) fuzzy systems have been studied. First, a new quadratic stability condition, which is more simple than that in a previous paper, has been proposed. Second, two new sufficient conditions in the terms of linear matrix inequalities (LMIs) which guarantee the existence of the state feedback H/sub /spl infin// control for the T-S fuzzy systems have been proposed. The conditions are not only simple but also consider the interactions among the fuzzy subsystems. Finally, based on the LMIs, the H/sub /spl infin// controller designing methods for the T-S fuzzy systems have been given.
03 Dec 2003
TL;DR: This research work proposes the utilization of the unsupervised Hebbian algorithm to nonlinear units for training FCMs and proposes the proposed learning procedure, which modifies its fuzzy causal web as causal patterns change and as experts update their causal knowledge.
Abstract: Fuzzy Cognitive Map (FCM) is a soft computing technique for modeling systems. It combines synergistically the theories of neural networks and fuzzy logic. The methodology of developing FCMs is easily adaptable but relies on human experience and knowledge, and thus FCMs exhibit weaknesses and dependence on human experts. The critical dependence on the expert’s opinion and knowledge, and the potential convergence to undesired steady states are deficiencies of FCMs. In order to overcome these deficiencies and improve the efficiency and robustness of FCM a possible solution is the utilization of learning methods. This research work proposes the utilization of the unsupervised Hebbian algorithm to nonlinear units for training FCMs. Using the proposed learning procedure, the FCM modifies its fuzzy causal web as causal patterns change and as experts update their causal knowledge.
01 Mar 2003
TL;DR: An adaptive fuzzy sliding mode controller for robotic manipulators using an adaptive single-input single-output (SISO) fuzzy system is applied to calculate each element of the control gain vector in a sliding mode controllers based on the Lyapunov method.
Abstract: This paper proposes an adaptive fuzzy sliding mode controller for robotic manipulators. An adaptive single-input single-output (SISO) fuzzy system is applied to calculate each element of the control gain vector in a sliding mode controller. The adaptive law is designed based on the Lyapunov method. Mathematical proof for the stability and the convergence of the system is presented. Various operation situations such as the set point control and the trajectory control are simulated. The simulation results demonstrate that the chattering and the steady state errors, which usually occur in the classical sliding mode control, are eliminated and satisfactory trajectory tracking is achieved.
TL;DR: It is shown that the control laws can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software.
Abstract: This paper presents a kind of controller synthesis method for fuzzy dynamic systems based on a piecewise smooth Lyapunov function. The basic idea of the proposed approach is to construct controllers for the fuzzy dynamic systems in such a way that a piecewise continuous Lyapunov function can be used to establish the global stability with H/sub /spl infin// performance of the resulting closed loop fuzzy control systems. It is shown that the control laws can be obtained by solving a set of linear matrix inequalities that is numerically feasible with commercially available software. An example is given to illustrate the application of the proposed methods.
01 Apr 2003
TL;DR: An adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented and the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design approach.
Abstract: A new design approach of an adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. A fuzzy terminal sliding mode controller is designed to retain the advantages of the terminal sliding mode controller and to reduce the chattering occurred with the terminal sliding mode controller. The sufficient condition is provided for the uncertain system to be invariant on the sliding surface. The parameters of the output fuzzy sets in the fuzzy mechanism are adapted on-line to improve the performance of the fuzzy sliding mode control system. The bounds of the uncertainties are not required to be known in advance for the presented adaptive fuzzy sliding mode controller. The stability of the fuzzy control system is also guaranteed. Moreover, the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed adaptive fuzzy terminal sliding mode controller.
TL;DR: An ACC controller based on fuzzy logic is presented, which assists the speed and distance vehicle control, offering driving strategies and actuation over the throttle of a car, embedded in an automatic driving system installed in two testbed mass-produced cars.
Abstract: There is a broad range of diverse technologies under the generic topic of intelligent transportation systems (ITS) that holds the answer to many of the transportation problems. In this paper, one approach to ITS is presented. One of the most important research topics in this field is adaptive cruise control (ACC). The main features of this kind of controller are the adaptation of the speed of the car to a predefined one and the keeping of a safe gap between the controlled car and the preceding vehicle on the road. We present an ACC controller based on fuzzy logic, which assists the speed and distance vehicle control, offering driving strategies and actuation over the throttle of a car. The driving information is supplied by the car tachometer and a RTK differential GPS, and the actuation over the car is made through an electronic interface that simulates the electrical signal of the accelerator pedal directly to the onboard computer. This control is embedded in an automatic driving system installed in two testbed mass-produced cars instrumented for testing the work of these controllers in a real environment. The results obtained in these experiments show a very good performance of the gap controller, which is adaptable to all the speeds and safe gap selections.
TL;DR: In this paper, the concepts of car maneuvers, fuzzy logic control (FLC), and sensor-based behaviors are merged to implement the human-like driving skills by an autonomous car-like mobile robot (CLMR).
Abstract: In this paper, the concepts of car maneuvers, fuzzy logic control (FLC), and sensor-based behaviors are merged to implement the human-like driving skills by an autonomous car-like mobile robot (CLMR). Four kinds of FLCs, fuzzy wall-following control, fuzzy corner control, fuzzy garage-parking control, and fuzzy parallel-parking control, are synthesized to accomplish the autonomous fuzzy behavior control (AFBC). Computer simulation results illustrate the effectiveness of the proposed control schemes. The setup of the CLMR is provided, where the implementation of the AFBC on a field-programmable gate array chip is also addressed. Finally, the real-time implementation experiments of the CLMR in the test ground demonstrate the feasibility in practical car maneuvers.
10 Nov 2003
TL;DR: A rigorous theoretic proof is given to show that the proposed quadratic stabilization condition can include previous results as special cases and is not only suitable for designing fuzzy state feedback controllers but also convenient for fuzzy static output feedback controller design.
Abstract: This paper proposes a new quadratic stabilization condition for T-S fuzzy control systems. The condition is represented in the form of linear matrix inequalities (LMIs) and is shown to be less conservative than some relaxed quadratic stabilization conditions published recently in the literature. A rigorous theoretic proof is given to show that the proposed condition can include previous results as special cases. In comparison with conventional conditions, the proposed condition is not only suitable for designing fuzzy state feedback controllers but also convenient for fuzzy static output feedback controller design. The latter design work is quite hard for T-S fuzzy control systems. Based on the LMI-based conditions derived, one can easily synthesize controllers for stabilizing T-S fuzzy control systems. Since only a set of LMIs is involved, the controller design is quite simple and numerically tractable.
TL;DR: This paper presents a robust adaptive fuzzy neural controller suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems.
Abstract: This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.
TL;DR: A neuro-fuzzy controller for sensor-based mobile robot navigation in indoor environments and the control system consists of a hierarchy of robot behaviors.
Abstract: This paper discusses a neuro-fuzzy controller for sensor-based mobile robot navigation in indoor environments. The control system consists of a hierarchy of robot behaviors.
TL;DR: It is concluded that as GIS–related applications increase in their levels of complexity and sophistication fuzzy sets will play a major, cost effective role in their development.
Abstract: The development of fuzzy sets in geographic information systems (GIS) arose out of the need to handle uncertainty and the ability of soft computing technology to support fuzzy information processing. An overview of the fundamentals of fuzzy sets is used to illustrate its use in GIS. The use of some terms within both the GIS and fuzzy information processing community is clarified. Since one of the key problems when applying fuzzy sets to GIS problems is in the specification of grades of membership, the many methods used to specify memberships in fuzzy sets in GIS applications are presented. The α - cut is defined and shown to be of increasing importance in GIS. Non-compensatory and compensatory connectives are compared. Aggregation oper- ators are reviewed and shown to be useful in a number of GIS studies. Fuzzy relations and fuzzy control systems are briefly discussed with reference to their use in GIS and in relation to the development of modern soft computing technology. Several features of fuzzy sets make that paradigm attractive for use in GIS. It is concluded that as GIS-related applications increase in their levels of complexity and sophistication fuzzy sets will play a major, cost effective role in their development.
TL;DR: An adaptive fuzzy sliding mode controller is proposed to suppress the sprung mass position oscillation due to road surface variation and this intelligent control strategy combines an adaptive rule with fuzzy and sliding mode control algorithms.
Abstract: Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate model for designing a model-based controller. Here, an adaptive fuzzy sliding mode controller is proposed to suppress the sprung mass position oscillation due to road surface variation. This intelligent control strategy combines an adaptive rule with fuzzy and sliding mode control algorithms. It has online learning ability to deal with the system time-varying and nonlinear uncertainty behaviors, and adjust the control rules parameters. Only eleven fuzzy rules are required for this active suspension system and these fuzzy control rules can be established and modified continuously by online learning. The experimental results show that this intelligent control algorithm effectively suppresses the oscillation amplitude of the sprung mass with respect to various road surface disturbances.
Book•
28 Feb 2003
TL;DR: New model structures and identification algorithms are described for the effective use of heterogeneous information in the form of numerical data, qualitative knowledge, and first principle models for model-based control.
Abstract: From the Publisher:
"This book presents new approaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effective use of heterogeneous information in the form of numerical data, qualitative knowledge, and first principle models. The main methods and techniques are illustrated through several simulated examples and real-world applications from chemical and process engineering practice." "Supporting MATLAB and Simulink files, available at the website www.fmt.vein.hu/softcomp, create a computational platform for exploration and illustration of many concepts and algorithms presented in the book." The book is aimed primarily at researchers, practitioners, and professionals in process control and identification, but it is also accessible to graduate students in electrical, chemical, and process engineering. Technical prerequisites include an undergraduate-level knowledge of control theory and linear algebra. Additional familiarity with fuzzy systems is helpful but not required.
TL;DR: An adaptive fuzzy robust tracking control (AFRTC) algorithm is proposed for a class of nonlinear systems with the uncertain system function and uncertain gain function, which are all the unstructured (or nonrepeatable) state-dependent unknown nonlinear functions arising from modeling errors and external disturbances.
Abstract: An adaptive fuzzy robust tracking control (AFRTC) algorithm is proposed for a class of nonlinear systems with the uncertain system function and uncertain gain function, which are all the unstructured (or nonrepeatable) state-dependent unknown nonlinear functions arising from modeling errors and external disturbances. The Takagi-Sugeno type fuzzy logic systems are used to approximate unknown uncertain functions and the AFRTC algorithm is designed by use of the input-to-state stability approach and small gain theorem. The algorithm is highlighted by three advantages: 1) the uniform ultimate boundedness of the closed-loop adaptive systems in the presence of nonrepeatable uncertainties can be guaranteed; 2) the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed; and 3) the adaptive mechanism with minimal learning parameterizations can be obtained. The performance and limitations of the proposed method are discussed. The uses of the AFRTC for the tracking control design of a pole-balancing robot system and a ship autopilot system to maintain the ship on a predetermined heading are demonstrated through two numerical examples. Simulation results show the effectiveness of the control scheme.
08 Dec 2003
TL;DR: The proposed approach is based on the particle swarm optimization method and it is used for the detection of proper weight matrices that lead the fuzzy cognitive map to desired steady states.
Abstract: We introduce a new algorithm for fuzzy cognitive maps learning. The proposed approach is based on the particle swarm optimization method and it is used for the detection of proper weight matrices that lead the fuzzy cognitive map to desired steady states. For this purpose a properly defined objective function that incorporates experts' knowledge is constructed and minimized. The application of the proposed methodology to an industrial control problem supports the claim that the proposed technique is efficient and robust.
TL;DR: A self-learning fuzzy sliding-mode control (SLFSMC) design method is proposed for ABS, where the tuning algorithms of the controller are derived in the Lyapunov sense and the stability of the system can be guaranteed.
Abstract: The antilock braking system (ABS) is designed to optimize braking effectiveness and maintain steerability; however, the ABS performance will be degraded in the case of severe road conditions. In this study, a self-learning fuzzy sliding-mode control (SLFSMC) design method is proposed for ABS. The SLFSMC ABS will modulate the brake torque for optimum braking. The SLFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to compensate for the approximation error between the ideal controller and the fuzzy controller. The tuning algorithms of the controller are derived in the Lyapunov sense; thus, the stability of the system can be guaranteed. Also, the derivation of the proposed SLFSMC ABS does not need to use a vehicle-braking model. Simulations are performed to demonstrate the effectiveness of the proposed SLFSMC ABS in adapting to changes for various road conditions.
23 Mar 2003
TL;DR: Using the fuzzy control model, an offloading inference engine is developed to adaptively solve two key decision-making problems during runtime offloading: timely triggering of adaptive offloading, and intelligent selection of an application partitioning policy.
Abstract: Pervasive computing allows a user to access an application on heterogeneous devices continuously and consistently. However it is challenging to deliver complex applications on resource-constrained mobile devices, such as cellular telephones and PDA. Different approaches, such as application-based or system-based adaptations, have been proposed to address the problem. However existing solutions often require degrading application fidelity. We believe that this problem can be overcome by dynamically partitioning the application and offloading part of the application execution to a powerful nearby surrogate. This will enable pervasive application delivery to be realized without significant fidelity degradation or expensive application rewriting. Because pervasive computing environments are highly dynamic, the runtime offloading system needs to adapt to both application execution patterns and resource fluctuations. Using the fuzzy control model, we have developed an offloading inference engine to adaptively solve two key decision-making problems during runtime offloading: (1) timely triggering of adaptive offloading, and (2) intelligent selection of an application partitioning policy. Extensive trace-driven evaluations show the effectiveness of the offloading inference engine.
TL;DR: In this article, the problem of designing a filter for a class of fuzzy dynamical systems that guarantees that the L/sub 2/ gain from an exogenous input to a filter error is less or equal to a prescribed value and the filter is quadratically stable in a prespecified linear matrix inequality (LMI) stability region is addressed.
Abstract: This brief addresses the problem of designing a filter for a class of fuzzy dynamical systems that guarantees that: 1) the L/sub 2/ gain from an exogenous input to a filter error is less or equal to a prescribed value and 2) the filter is quadratically stable in a prespecified linear matrix inequality (LMI) stability region. Based on an LMI approach, solutions to the problem of the H/sub /spl infin// fuzzy filtering with quadratic D stability are derived in terms of a family of LMIs. Numerical simulation examples are presented to illustrate the theory development.
TL;DR: The preliminary research and implementation of a fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking systems (ABSs) indicate that ABS/traction control may substantially improve longitudinal performance and offer significant potential for optimal control of driven wheels, especially under icy conditions.
Abstract: The application of fuzzy-based control strategies has gained enormous recognition as an approach for the rapid development of effective controllers for nonlinear time-variant systems. This paper describes the preliminary research and implementation of a fuzzy logic based controller to control the wheel slip for electric vehicle antilock braking systems (ABSs). As the dynamics of the braking systems are highly nonlinear and time variant, fuzzy control offers potential as an important tool for development of robust traction control. Simulation studies are employed to derive an initial rule base that is then tested on an experimental test facility representing the dynamics of a braking system. The test facility is composed of an induction machine load operating in the generating region. It is shown that the torque-slip characteristics of an induction motor provides a convenient platform for simulating a variety of tire/road /spl mu/-/spl sigma/ driving conditions, negating the initial requirement for skid-pan trials when developing algorithms. The fuzzy membership functions were subsequently refined by analysis of the data acquired from the test facility while simulating operation at a high coefficient of friction. The robustness of the fuzzy-logic slip regulator is further tested by applying the resulting controller over a wide range of operating conditions. The results indicate that ABS/traction control may substantially improve longitudinal performance and offer significant potential for optimal control of driven wheels, especially under icy conditions where classical ABS/traction control schemes are constrained to operate very conservatively.
01 Feb 2003
TL;DR: A neural fuzzy system with mixed coarse learning and fine learning phases is proposed, which is able to perform collision-free navigation and a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration.
Abstract: Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system.