Written by two authors who have been involved in creating theoretical foundations for the field, who have helped assess the value of this new technology relative to conventional approches, and who have worked extensively with industry on implementations, Fuzzy Control is filled with a wealth of examples and case studies. The case studies demonstrate design and implementation techniques for a variety of applications including automotive systems, robotics, and aircraft.
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Fuzzy Control is a practical alternative for a variety of challenging control applications since it provides a convenient method for constructing nonlinear controllers via the use of heuristic information. Such heuristic information may come from an operator who has acted as a "human-in-the-loop" controller for a process. In the fuzzy control design methodology, we ask this operator to write down a set of rules on how to control the process, then we incorporate these into fuzzy controller that emulates the decision-making process of the human. In other cases, the heuristic information may come form a control engineer who has performed extensive mathematical modeling, analysis, and development of control algorithms for a particular process. Again, such expertise is loaded into the fuzzy controller to automate the reasoning processes and actions of the expert. Regardless of where the heuristic control knowledge comes from, fuzzy control provides a user-friendly formalism for representing and implementing the ideas we have about how to achieve high-performance control.
In this book we provide a control-engineering perspective on fuzzy control. We are concerned with both the construction of nonlinear controllers for challenging real-world application and with gaining a fundamental understanding of the dynamics of fuzzy control systems so that we can mathematically verify their properties (e.g., stability) before implementation. We emphasize engineering evaluations of performance and comparative analysis with conventional control methods. We introduce adaptive methods for identification, estimation, and control. We examine numerous examples, applications, and design and implementation case studies throughout the text. Moreover, we provide introductions to neural networks, genetic algorithms, expert and planning systems, and intelligent autonomous control, and explain how these topics relate to fuzzy control.
Overall, we take a pragmatic engineering approach to the design, analysis, performance evaluation, and implementation of fuzzy control systems. We are not concerned with whether the fuzzy controller is "artificially intelligent" or with investigating the mathematics of fuzzy sets (although some of the exercises do), but rather with whether the fuzzy control methodology can help solve challenging real-world problems.
Overview of the Book
The book is basically broken into three parts. In Chapters 1-4 we cover the basics of "direct" fuzzy control (i.e., the nonadaptive case). In Chapters 5-7 we cover adaptive fuzzy systems for estimation, identification, and control. Finally, in Chapters 5 - 7 we cover adaptive fuzzy systems for estimation, identification, and control. Finally, in Chapter 8 we briefly cover the main areas of intelligent control and highlight how the topics covered in this book relate to these areas. Overall, we largely focus on what one could call the " heuristic approach to fuzzy control" as opposed to the more recent mathematical focus on fuzzy control where stability analysis is a major theme.
In Chapter 1 we provide and overview of the general methodology for conventional control system design. Then we summarize the fuzzy control system design process and contrast the two. Next, we explain what this book is about via a simple motivating example. In Chapter 2 we first provide a tutorial introduction to fuzzy control via a two-input, one output fuzzy control design example. Following this we introduce a general mathematical characterization of fuzzy systems and study their fundamental properties. We use a simple inverted pendulum example to illustrate some of the most widely used approaches to fuzzy control system design. We explain how to write a computer program to simulate a fuzzy control system design. We explain how to write a computer program to simulate a fuzzy control system, using either a high-level language or Matlab. In the web and ftp pages for the book we provide such a code in C and Matlab. In Chapter 3 we use several case studies we pay particular attention to comparative analysis with conventional approaches. In Chapter 4 we show how to perform stability analysis of fuzzy control systems using Lyapunov methods and frequency domain-based stability criteria. We introduce nonlinear analysis methods that can be used to predict and eliminate steady-state tracking error and limit cycles. We then show how to use the analysis approaches in fuzzy control system design. The overall focus for these nonlinear analysis methods is on understanding fundamental problems that can be encountered in the design of fuzzy control systems and how to avoid them.
In Chapter 5 we introduce the basic "function approximation problem" and show how identification, estimation, prediction, and some control design problems are a special case of it. We show how to incorporate heuristic information into the function approximator. We show how one clustering method from fuzzy systems from data pairs and show how to train fuzzy systems from input-output data with least squares, gradient, and clustering methods. And we show how one clustering method from fuzzy pattern recognition can be used in conjunction with least squares methods to construct a fuzzy model from input-output data. Moreover, we discuss hybrid approaches that involve a combination of two or more of these methods. In Chapter 6 we introduce adaptive fuzzy control. First, we introduce several methods for automatically synthesizing and tuning a fuzzy controller, and then we illustrate their application via several design and implementation case studies. We also show how to tune a fuzzy model of the plant and use the parameters of such a model in the on-line design of a controller. In Chapter 7 we introduce fuzzy supervisory control. We explain how fuzzy systems can be used to automatically tune proportional-integral-derivative (PID) controllers, how fuzzy systems provide a methodology for constructing and implementing gain schedulers, and how fuzzy systems can be used to coordinate the application and tuning of conventional controllers. Following this, we show how fuzzy systems can be used to tune direct and adaptive fuzzy controllers. We provide case studies in the design and implementation of fuzzy supervisory control.
In Chapter 8 we summarize our control engineering perspective on fuzzy control, provide and overview of the other areas of the field of "intelligent control," and explain how these other areas relate to fuzzy control. In particular, we briefly cover neural networks, genetic algorithms, knowledge-based control (expert systems and planning systems), and hierarchical intelligent autonomous control.
Examples, Applications, and Design and Implementation Case Studies
We provide several design and implementation case studies for a variety of applications, and many examples are used throughout the text. The basic goals of these case studies and examples are as follows:
To help illustrate the theory.
To show how to apply the techniques.
To help illustrate design procedures in a concrete way.
To show what practical issues are encountered in the development and implementation of a fuzzy control system.
Some of the more detailed applications that are studied in the chapters and their accompanying homework problems are the following:
Direct fuzzy control: Translation inverted pendulum, fuzzy decision-making systems, two-link flexible robot, rotational inverted pendulum, and machine scheduling (Chapters 2 and 3 homework problems: translation inverted pendulum, automobile cruise control, magnetic ball suspension system, automated highway system, single-link flexible robot, rotational inverted pendulum, machine scheduling, motor control, cargo ship steering, base braking control system, rocket velocity control, acrobot, and fuzzy decision making systems).
Nonlinear analysis: Inverted pendulum, temperature control, hydrofoil controller, underwater vehicle control, and tape drive servo (Chapter 4 homework problems: inverted pendulum, magnetic ball suspension system, temperature control and hydrofoil controller design).
Fuzzy identification and estimation: Engineer intake manifold failure estimation, and failure detection and identification for internal combustion engine calibration faults (Chapter 5 homework problems: tank identification, engine friction estimation, and cargo ship failures estimation).
Adaptive fuzzy control: Two-link flexible robot, cargo ship steering, fault tolerant aircraft control, magnetically levitated ball, rotational inverted pendulum, machine scheduling, and level control in a tank (Chapter 6 homework problems: tanker and cargo ship steering, liquid level control in a tank, rocket velocity control, base braking control system, magnetic ball suspension system, rotational inverted pendulum, and machine scheduling).
Supervisory fuzzy control: Two-link flexible robot, and fault-tolerant aircraft control (Chapter 7 homework problems: liquid level control, and cargo and tanker ship steering).
Some of the applications and examples are dedicated to illustrating one idea from theory or one technique. Others are used in several places throughout the text to show how techniques build on one another and compare to each other. Many of the applications show how fuzzy control techniques compare to conventional control methodologies.
World Wide Web Site and FTP Site: Computer Code Available
The following information is available electronically:
Various versions of C and Matlab code for simulation of fuzzy controllers, fuzzy control systems, adaptive fuzzy identification and estimation methods, and adaptive fuzzy control systems (e.g., for some examples and homework problems in the text).
Other special notes of interest, including an errata sheet if necessary.
You can access this information via the web site:
or you can access the information directly via anonymous ftp to:
For anonymous ftp, log into the above machine with a username "anonymous" and use your e-mail address as a password.
Organization, Prerequisite, and Usage
Each chapter include and overview, a summary, and a section "For Further Study" that explains how the reader can continue study in the topical area of the chapter. At the end of each chapter overview, we explain how the chapter is related to the others. This includes an outline of what must be covered to be able to understand the later chapters and what may be skipped on a first reading. The summaries at the end of each chapter provide a list of all major topics covered in that chapter so that it is clear what should be learned in each chapter.
Each chapter also includes a set of exercises or design problems and often both. Exercises or design problems that are particularly challenging (considering how far along you are in the text) or that require you to help define part of the problem are designated with a star ("*")after the title of the problem. In addition to helping to solidify the concepts discussed in the chapters, the problems at the ends of the chapters are sometimes used to introduce new topics. We require the use of computer-aided design (CAD) for fuzzy controllers in many of the design problems at the ends of the chapters (e.g., via the use of Matlab or some high-level language).
The necessary background for the book includes courses on differential equations and classical control (root locus, Bode plots, Nyquist theory, lead-lag compensation, and state feedback concepts including linear quadratic regulator design). Courses on nonlinear stability theory and adaptive control would be helpful but are not necessary. Hence, much of the material can be covered in an undergraduate course as they require very little background besides a basic understanding of signals and systems including Laplace and z-transform theory (one application in Chapter 3 does, however, require a cursory knowledge of the linear quadratic regulator). Also, many parts of Chapters 5-7 can be covered once a student has taken a first course in control (a course in nonlinear control would be helpful for Chapter 4 but is not necessary). One could cover the basics of fuzzy control by adding parts of Chapter 2 to the end of a standard undergraduate or graduate course on control. Basically, however, we view the book as appropriate for a first-level graduate course in fuzzy control.
Alternatively, the text could be used for a course on intelligent control. In this case, the instructor could cover the material in Chapter 8 on neural networks and genetic algorithms after Chapter 2 or 3, then explain their role in the topics covered in Chapters 5, 6, and 7 while these chapters are covered. For instance, in Chapter 5 the instructor would explain how gradient and least squares methods can be used to train neural networks. In Chapter 6 the instructor cold draw analogies between neural control via the radial basis function neural networks can be used as the nonlinearity that is trained to act like the plant. In Chapter 7 the instructor could explain how neural networks can be trained to serve as gain schedulers. After Chapter 7 the instructor could then cover the material on expert control, planning systems, and intelligent autonomous control in Chapter 8. Many more details on strategies for teaching the material in a fuzzy or intelligent control course are given in the instructor's manual, which is described below.
Engineers and scientists working in industry will find that the book will serve nicely as a "handbook" for the development of fuzzy control systems, and that the design, simulation, and implementation case studies will provide very good insights into how to construct fuzzy controllers for specific applications. Researchers in academia and elsewhere will find that this book will provide and up-to-date view of the field, show the major approaches, provide good references for further study, and provide a nice outlook for thinking about future research directions.
An Instructor's Manual to accompany this textbook is available (to instructors only) from Addison Wesley Longman. The Instructor's Manual contains the following:
Strategies for teaching the material. Solutions to end-of-chapter exercises and design problems.
A description of a laboratory course that has been taught several times at The Ohio State University which can be run in parallel with a lecture course that is taught out of this book.
An electronic appendix containing the computer code (e.g., C and Matlab code) for solving many exercises and design problems.
Sales Specialists at Addison Wesley Longman will make the instructor's manual available to qualified instructors. To find out who your Addison Wesley Longman Sales Specialist is please see the web site:
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Feedback on the Book
It is our hope that w...From the Back Cover:
* The book contains a tutorial introduction to fuzzy control.
* A number of techniques commonly found in the industry are also provided, such as fuzzy estimation methods, adaptive methods, fuzzy supervisory control, and intelligent control methods.
* Computer code can be downloaded for solving the book's examples and problems and can be easily modified to implement the reader's own fuzzy controllers or estimators.
The book contains: * A tutorial introduction to fuzzy control
* Design and implementation case studies for automotive systems, robotics, aircraft, and others
* Nonlinear analysis of fuzzy control systems (e.g., stability analysis)
* Fuzzy estimation methods that are useful in signal processing and control
* Adaptive methods for automatic synthesis and tuning of fuzzy controllers
* Fuzzy supervisory control techniques that are increasingly finding use in industry
* Intelligent control methods (e.g., neural networks, genetic algorithms)
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Book Description Addison Wesley Publishing Comp, 1997. Hardcover. Book Condition: New. Never used!. Bookseller Inventory # P11020118074X