Fuzzy Control and Identification

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9780470542774: Fuzzy Control and Identification

This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models.

Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.

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From the Back Cover:

A comprehensive introduction to fuzzy control and identification, covering both Mamdani and Takagi-Sugeno fuzzy systems

A fuzzy control system is a control system based on fuzzy logic, which is a mathematical system that makes decisions using human reasoning processes. This book presents an introductory-level exposure to two of the principal uses for fuzzy logic—identification and control. Drawn from the author's lectures presented in a graduate-level course over the past decade, this volume serves as a holistically suitable single text for a fuzzy control course, compiling the information often found in several different books on the subject into one.

Starting with explanations of fuzzy logic, fuzzy control, and adaptive fuzzy control, the book introduces the concept of expert knowledge, which is the basis for much of fuzzy control. From there, the author covers:

  • Basic concepts of fuzzy sets such as membership functions, universe of discourse, linguistic variables, linguistic values, support, a-cut, and convexity

  • Both Mamdani and Takagi-Sugeno fuzzy systems, showing how an effective controller can be designed for many complex nonlinear systems without mathematical models or knowledge of control theory while also suggesting several approaches to modeling of complex engineering systems with unknown models

  • How PID controllers can be made fuzzy and why this is useful

  • Position-form and incremental-form fuzzy controllers

  • How nonlinear systems can be modeled as fuzzy systems in several forms

  • How fuzzy tracking control and model reference control can be realized for nonlinear systems using parallel distributed techniques

  • The estimation of nonlinear systems using the batch least squares, recursive least squares, and gradient methods

  • The creation of direct and indirect adaptive fuzzy controllers

Also included are many examples, exercises, and computer program listings, all class-tested. Fuzzy Control and Identification is intended for seniors and first-year graduate students, and is suitable for any engineering department. No knowledge specific to any particular branch of engineering is required, and no knowledge of electrical, chemical, or mechanical systems is necessary to read and understand the material.

About the Author:

John H. Lilly, PhD, is a professor in the Speed School of Engineering at the University of Louisville. His research interests are nonlinear and adaptive control, fuzzy identification and control, positive/negative fuzzy systems, pneumatic muscle actuators, and robotics. In addition to his twenty-eight years of teaching experience, Dr. Lilly has written more than fifty refereed journal and conference articles, book chapters, invited scholarly lectures, and seminars.

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Book Description John Wiley and Sons Ltd, United States, 2010. Hardback. Book Condition: New. 1. Auflage. Language: English . Brand New Book. This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic. Bookseller Inventory # AAH9780470542774

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