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Fuzzy Control and Identification, by John H. Lilly
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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.
- Sales Rank: #2532252 in eBooks
- Published on: 2011-05-12
- Released on: 2011-05-12
- Format: Kindle eBook
Review
“This is a very useful and attractive material on fuzzy sets in control engineering-accessible to large categories of readers … The book is equally recommended to students (who want to become familiar with the fuzzy logic approach), educators (who are looking for a reliable course and / or application support) and practitioners (who are interested in enlarging their professional horizon).” (Zentralblatt MATH, 2012)
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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:
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Basic concepts of fuzzy sets such as membership functions, universe of discourse, linguistic variables, linguistic values, support, a-cut, and convexity
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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
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How PID controllers can be made fuzzy and why this is useful
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Position-form and incremental-form fuzzy controllers
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How nonlinear systems can be modeled as fuzzy systems in several forms
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How fuzzy tracking control and model reference control can be realized for nonlinear systems using parallel distributed techniques
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The estimation of nonlinear systems using the batch least squares, recursive least squares, and gradient methods
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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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
concise descriptions of fuzzy ideas and applications
By W Boudville
Lily offers the reader a concise introduction to the use of fuzzy sets in control systems. The text assumes some previous acquaintance with the basic ideas of control systems theory. It explicitly affirms that fuzzy control is best suited when the systems are nonlinear. Now a reader who has already dealt with those systems could suggest that if a model of that nonlinear system is made, then perhaps it could be used in a control loop without any fuzzy concepts. But the text points out that often no such model is made. Possibly due to the complexity of the system. And even when a nonlinear model has been constructed, it might be [and probably is] an approximation to the underlying reality. So even in this case, there is scope for a fuzzy approach to be useful, where it keys off human expert knowledge and might not require much modelling.
From a modular perspective, chapter 3 is useful in showing how to go from an input of precise ['crisp'] values of some independent variable to fuzzy sets, which are then used in a controller that makes fuzzy intermediate output. This is then defuzzified into precise dependent output values. Later chapters expand upon this, by looking more closely at how the controller functions.
The book is directed at a senior level undergraduate readership, and furnishes problem sets in each chapter to this ends. The level of maths is moderately complex. The idea of using a transfer function should already be familiar to the reader, along with some matrix algebra. The overt use of probability theory is minimal.
The book makes the choice of Matlab, as a programming toolkit, and Matlab example code is offered in the appendix. A reader who prefers another toolkit [Mathematica, say] should be able to manually translate the source code into equivalent code of that toolkit. So don't take the choice of Matlab as being unduly restrictive.
0 of 0 people found the following review helpful.
A Very Readable Introduction for Fuzzy Controls
By Walter W. Olson, Ph.D, P.E.
This a good introduction into Fuzzy logic Controllers for an applications controls engineer. It is a complete text book with good practical examples. The book is well written and easy to understand. In fact I am considering redesigning the early week of the controls course I teach to use this presentation of the Mamdani Controller as it does not need an extensive controls background beyond basic definitions and demonstrates what we are attempting to accomplish with a control system. Takagi Sugeno systems are discussed but from a practical application rather than mathematical theory. I highly recommend this book to engineers that want an initial introduction to Fuzzy Control Systems.
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