The book, as a whole, will definitely be an asset when introducing students to the exciting field of adaptive control. A direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control, is capable of incorporating fuzzy ifthen control rules directly into the controllers, and guarantees the global stability of the resulting closedloop system in the sense that all signals involved are uniformly bounded is developed. Observerbased adaptive fuzzy sliding mode control for. The book also provides rigorous analysis of nonlinear fuzzy control systems, and outlines a simple method to guarantee the stability of nonlinear control systems. A highly accessible and unified approach to the design and analysis of intelligent control systems adaptive approximation based control is a tool every control designer should have in his or her control toolbox. In the simulation, two examples including a comparison with the traditional integer.
Dec 01, 2020 the results of theoretical models and numerical simulation are helpful for better understanding of other similar nonlinear financial risk dynamic systems. Under the framework of the backstepping control design and fuzzy adaptive control, a new adaptive fuzzy output tracking control method is developed. It summarizes the stateoftheart methods for automatic tuning of the parameters and structures of fuzzy logic systems. The experimental results verified that the proposed approach can achieve excellent control performance despite external disturbance. This volume develops a variety of adaptive fuzzy systems and applies them to a. The stability analysis and fuzzy controller design problem of discrete ts fuzzy control system are discussed based on fuzzy lyapunov function. In this article, an adaptive fuzzy sliding mode control afsmc scheme is derived for robotic systems. In chapter 1 we provide an overview of the general methodology for conventional control system design.
The stability analysis of fuzzy control systems is one of the important concepts in the analysis of control systems. Design and stability analysis administrative history of the johnson electrical engineering electrical engineering. Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. Rigatos intelligent robotics and automation laboratory, department of electrical and computer engineering, national technical university of athens, zografou, 15773 athens, greece abstract a hybrid intelligent control. In our case it is implemented to a continuous stirredtank simulated reactor and compared to optimal pi control. The central subject of this book is a systematic framework for the stability and design of nonlinear fuzzy control systems. Design and stability analysis of supervisorbased adaptive. Fuzzy system identification and adaptive control ruiyun. The dynamic model of this mls is first constructed based on the. Vidyasagar, nonlinear systems analysis, prenticehall, engelwood cli s, nj, 1993.
Farinwata ford motor company, research laboratory, dearborn, michigan, usa dimitar filev ford motor company, amtdc, redford, michigan, usa reza langari texas a m university, college station, texas, usa fuzzy techniques are used to cope with imprecision in the basic elements of a process under control. Design and stability analysis of fuzzy modelbased predictive. Ying, su cient conditions on general fuzzy systems as function approximators, automatica 30 1994 521525. Pdf stability analysis and design of fuzzy control systems. Jan 24, 1992 fuzzy sets and systems 45 1992 5156 5 northholland stability analysis and design of fuzzy control systems kazuo tanaka and michio sugeno department of systems science, tokyo institute of technology, 4259 nagatsuta, midoriku, yokohama 227, japan received november 1989 revised may 1990 abstract. An application of fuzzy systems to nonlinear system adaptive control design is proposed in.
Stability analysis and design of fuzzy control systems. The adaptive algorithms and system stability analysis are presented in sections iv and v for both indirect and direct schemes. Observer design according to the description of the fuzzy logic system presented in section 2. To train a fuzzy system using neuro adaptive methods, you must collect inputoutput training data using experiments or simulations of the system you want to model. It shows, step by step, how to combine linguistics and numerical information using various kinds of adaptive fuzzy systems. Sep 12, 2001 fuzzy control systems design and analysis. The stability analysis and the design technique of fuzzy control systems using fuzzy block.
The fractional order calculus is employed in the parameter updating stage. Stable adaptive fuzzy control of nonlinear systems ieee. The controller is based on the fusion of a sliding mode controller and an adaptive fuzzy system, adaptive exhibits and robust features. By using a class of triangular membership functions nearest to the origin and a simple adaptive law, the adaptive fuzzy control term is converted to an equivalence control term, which is used to facilitate stability analysis. Here, the basic control loop with a linear controller, for example a pid controller, is left unchanged. This paper presents a robust adaptive fuzzy sliding mode control method for a class of uncertain nonlinear systems. Sep 02, 2019 where is the membership function of the fuzzy set. Pdf an adaptive fuzzy sliding mode control scheme for. Feedback linearization techniques for nonlinear control system design have been developed in the last two decades 2, 3. Stability analysis of adaptive fuzzy control systems. Beside the selftuning control system and the model reference control system, other various types of adaptive control systems emerge endlessly, for example, variable structure control system, nonlinear adaptive control system, fuzzy adaptive control system and neural network adaptive control system etc. A systematic design procedure for fuzzy linguistic controllers with adaptive or learning capability is introduced. Stability analysis and control design are performed to both systems.
However, because it is fundamentally model free, conventional flc suffers from a lack of tools for systematic stability analysis and controller design. This volume develops a variety of adaptive fuzzy systems and applies them to a variety of engineering problems. Sun q, li r and zhang p 2003 stable and optimal adaptive fuzzy control of complex systems using fuzzy dynamic model, fuzzy sets and systems, 3. Adaptive control strategy using lyapunov stability theory. Adaptive control design and analysis wiley online books. Therefore, it is more hard and challenging to address the problems of controller design and stability analysis in the purefeedback systems. Fuzzy modelbased predictive control is potentially interesting in the case of batch reactors, heatexchangers, furnaces and all the processes with strong nonlinear dynamics and high transport delays.
Design and analysis of an adaptive fuzzy power system. Jun 25, 2003 as the advantages of adaptive systems for developing advanced applications grow apparent, adaptive control is becoming more popular in many fields of engineering and science. In this paper, an adaptive fuzzy backstepping output feedback dynamic surface control dsc approach is developed for a class of multiinput and multioutput mimo stochastic nonlinear systems with immeasurable states. Fuzzy sets and systems 45 1992 5156 5 northholland stability analysis and design of fuzzy control systems kazuo tanaka and michio sugeno department of systems science, tokyo institute of technology, 4259 nagatsuta, midoriku, yokohama 227, japan received november 1989 revised may 1990 abstract. We need at least a fuzzy model of an objective system in. Introduction robust control is essential for current industrial automation systems and will become even more important in applications like robotics, where future robots will be adopted for tasks in unstructured. That is the reason why recently, there have been significant research efforts in this direction. Takagisugeno fuzzy payload estimation and adaptive control. Adaptive fixedtime fuzzy control of uncertain nonlinear. In, the authors have solved the issue of fuzzy adaptive controller design and system stability analysis of fractionalorder uncertain nonlinear systems. It supervises and improves the operation of main the controller from upper control level using available system data different from the used by the main controller from extra sources operator or other systems. We can design theoretically a modelbased fuzzy controller if we have a useful stability criterion for fuzzy control systems.
Fuzzy control systems design and analysis wiley online books. Pdf stability analysis of an adaptive fuzzy control system. The book also provides rigorous analysis of nonlinear fuzzy control systems, and outlines a simple method to the stability of nonlinear control systems. Lyapunov function to guarantee control system stability. Adaptive fuzzy fast finitetime tracking control for. Jun 14, 2017 fuzzy logic control flc has proven to be a popular control methodology for many complex systems in industry, and is often used with great success as an alternative to conventional control techniques. Adaptive fuzzy sliding mode controller for the kinematic. The parameters of the linear controller are adapted to changing operating conditions gain scheduling. Adaptivefuzzy control compensation design for direct.
The objective of this paper is to introduce an adaptive fuzzy logic control system based on. Trove is a collaboration between the national library of australia and hundreds of partner organisations around australia. Observerbased adaptive fuzzy backstepping dynamic surface. A fuzzy based adaptive controller was designed for purefeedback nonlinear systems in. Then a controller is constructed assuming that the fuzzy logic system approximately represents the true plant. Fuzzy adaptive control for fractional nonlinear systems. The strength lies in the rigorous treatment of stability and convergence tools, fundamental to the understanding of adaptive algorithms. Adaptive fuzzy control for a class of mimo underactuated. Next, we explain what this book is about via a simple motivating example.
Asme 2010 10th biennial conference on engineering systems design and analysis, volume 5. Proceedings of the asme 2010 10th biennial conference on engineering systems design and analysis. There is no systematic method to design the number of rules and membership functions by now, and only the fuzzy logic control method may not guarantee satisfactory request. Furthermore, the adaptive fuzzy control for the fractionalorder financial risk chaotic system is investigated on the fractional lyapunov stability criterion. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated online. Lixin wang published in 1994 in englewood cliffs nj by prenticehall. Control algorithms based on fuzzy logic have been implemented in many processes 8,9.
This perception was articulated in my 1971 paper toward a theory of fuzzy systems, and 1972 paper, a rationale for fuzzy control. The underlying stability analysis as well as parameter update law design is carried out by lyapunov based technique. A fuzzy control system is a control system based on fuzzy logica mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values. Jul 15, 2014 this paper focuses on the design of a realtime adaptive takagisugeno ts fuzzy based dynamic feedback tracking controller to deal with the metallic sphere position control of a magnetic levitation system mls, which is an intricate and highly nonlinear system involving plant uncertainties and external disturbances. Pdf a systematic design procedure for fuzzy linguistic controllers with adaptive or learning capability.
A fuzzy control system is a control system based on fuzzy logica mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 true or false, respectively. In the field of modern industrial engineering, many mechanical systems are underactuated, exhibiting strong nonlinear characteristics and high flexibility. May 01, 2015 fuzzy logic systems are used to identify the unknown nonlinear system. Dynamical analysis and adaptive fuzzy control for the.
Adaptive fuzzy systems and control design and stability analysis. Considering the practical application of the proposed control strategy, a timevarying signal transmission delay is investigated. Since my background was in systems analysis, it did not take me long to realise that the theory of fuzzy sets is of substantial relevance to systems analysis and, especially, to control. Still, the chapters point to new exciting areas in adaptive control research. Additionally, some unavoidable practical issues, e. Mixing approximation theory, parameter estimation, and feedback control, this book presents a unified approach designed to enable readers to apply adaptive approximation based control. A slidingsurfacebased adaptive fuzzy control is designed for control compensation. Ysis by li xin wang, ptr prentice hall, englewood cliffs, nj, 232 pp. In indirect adaptive fuzzy control, the fuzzy logic systems are used to model the plant.
Design and analysis of an adaptive fuzzy power system stabilizer. Stable adaptive fuzzy controllers with application to inverted pendulum tracking. Tuning fuzzy control, pneumatic artificial muscles, functional approximation, lyapunov function 1. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption. Section vi describes the application to a jet engine control problem to illustrate the performance of the proposed adaptive neural fuzzy control methods. Design and stability analysis of adaptive fuzzy feedback. Mathematics and computers in simulation 51 2000 315339 stability analysis of an adaptive fuzzy co ntrol system using petri nets and learning automata s. In 32, 33, the authors presented the adaptive neural control for nonlinear purefeedback systems. The application of such control techniques has been motivated by the desire for one or more of the following. Design and stability analysis that you are looking for. But the design of fuzzy rules which is the center of fuzzy control depends largely on the experience and knowledge of experts. A widely used control scheme with a fuzzy system is parameter adaptive control. Adaptive fuzzy control for nonlinear systems with sampled. Adaptive neuralfuzzy control for interpolated nonlinear.
Fuzzy logic systems technology, particularly fuzzy control and fuzzy modeling techniques, is one of the most successful practical applications of fuzzy set and logic theory. Building on the socalled takagisugeno fuzzy model, a number of most important issues in fuzzy control systems are addressed. Design of fuzzy adaptive pid controller for nonlinear. Fuzzy system identification and adaptive control ruiyun qi. Adaptive fuzzy systems and control design and stability. In this paper, an adaptive fuzzy backstepping control strategy is studied for nonlinear nonstrict feedback systems with sampled data and timevarying input delay. This book gives a comprehensive treatment of modelbased fuzzy control systems. Adaptive fuzzy sliding mode control approach for swarm formation control of multiagent systems. Adaptive fuzzy sliding mode control approach for swarm. In 19, the authors discussed the robust adaptive control for nonlinear uncertain fractionalorder chaotic systems with external disturbances. Stability analysis and control design are performed to both systems using.
Design and stability analysis, prenticehall, engelwood cli s, nj, 1994. A comprehensive treatment of modelbased fuzzy control systems. Using a simple, balanced, and harmonious style, this book provides a convenient introduction to the subject and improves ones understanding of adaptive control theory. Adaptive fuzzy backstepping control design for a class of. By using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy estimator fe. Fuzzy logic systems are firstly utilized to approximate the unknown nonlinear functions, and then a fuzzy state observer is designed to estimate the immeasurable states. Adaptive fractional fuzzy sliding mode control for. However, the lack of control inputs brings about many difficulties for controller design and stability convergence analysis. Adaptive fuzzy control design acta polytechnica hungarica. In the afsmc design, the sliding mode control smc concept is combined with fuzzy control strategy to obtain a modelfree fuzzy sliding mode control. In general, anfis training works well if the training data is fully representative of the features of the data that the trained fis is intended to model. Park c 2003 lmibased robust stability analysis for fuzzy feedback linearization regulators with its applications, information sciences.
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