Dimiter Driankov - Böcker

This volume contains the thoroughly refereed and revised papers accepted for presentation at the IJCAI '91 Workshops on Fuzzy Logic and Fuzzy Control, held during the International Joint Conference on AI at Sydney, Australia in August 1991. The 14 technical contributions are devoted to several theoretical and applicational aspects of fuzzy logic and fuzzy control; they are presented in sections on theoretical aspects of fuzzy reasoning and fuzzy control, fuzzy neural networks, fuzzy control applications, fuzzy logic planning, and fuzzy circuits. In addition, there is a substantial introduction by the volume editors on the latest developments in the field that brings the papers presented into line.

Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as integral parts of a wide range of industrial process control systems and consumer products. Applications of fuzzy controllers are an established practice for Japanese manufacturers, and are spreading in Europe and America. The main aim of this book is to show that fuzzy control is not totally ad hoc, that there exist formal techniques for the analysis of a fuzzy controller, and that fuzzy control can be implemented even when no expert knowledge is available. The book is mainly oriented to control engineers and theorists, although parts can be read without any knowledge of control theory and may interest AI people. This 2nd, revised edition incorporates suggestions from numerous reviewers and updates and reorganizes some of the material.

Model Based Fuzzy Control uses a given conventional or fuzzy open loop model of the plant under control to derive the set of fuzzy rules for the fuzzy controller. Of central interest are the stability, performance, and robustness of the resulting closed loop system. The major objective of model based fuzzy control is to use the full range of linear and nonlinear design and analysis methods to design such fuzzy controllers with better stability, performance, and robustness properties than non-fuzzy controllers designed using the same techniques. This objective has already been achieved for fuzzy sliding mode controllers and fuzzy gain schedulers - the main topics of this book. The primary aim of the book is to serve as a guide for the practitioner and to provide introductory material for courses in control theory.

This carefully edited collection of recent works in fuzzy model identification opens the field to conventional control theorists as a complement to existing approaches, provides practicing engineers with new techniques, and emphasizes opportunities for new theory by bringing together different methods to identify the same types of fuzzy models. In control engineering, mathematical models are often constructed without using system data (white-box models) or using data but no insight (black-box models). The authors in this volume combine white- and black-box models chosen from types of structures known to be flexible and successful in applications. They use the same notation and terminology, and each describes a model with an identification technique and gives a practical example to show how the method works.

Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as integral parts of a wide range of industrial process control systems and consumer products. Applications of fuzzy controllers are an established practice for Japanese manufacturers, and are spreading in Europe and America. The main aim of this book is to show that fuzzy control is not totally ad hoc, that there exist formal techniques for the analysis of a fuzzy controller, and that fuzzy control can be implemented even when no expert knowledge is available. The book is mainly oriented to control engineers and theorists, although parts can be read without any knowledge of control theory and may interest AI people. This 2nd, revised edition incorporates suggestions from numerous reviewers and updates and reorganizes some of the material.

Model Based Fuzzy Control uses a given conventional or fuzzy open loop model of the plant under control to derive the set of fuzzy rules for the fuzzy controller. Of central interest are the stability, performance, and robustness of the resulting closed loop system. The major objective of model based fuzzy control is to use the full range of linear and nonlinear design and analysis methods to design such fuzzy controllers with better stability, performance, and robustness properties than non-fuzzy controllers designed using the same techniques. This objective has already been achieved for fuzzy sliding mode controllers and fuzzy gain schedulers - the main topics of this book. The primary aim of the book is to serve as a guide for the practitioner and to provide introductory material for courses in control theory.

Model-based fuzzy control uses a given conventional or a fuzzy open loop of the plant under control in order to derive the set of fuzzy if-then rules constituting the corresponding fuzzy controller.

The goal of autonomous mobile robotics is to build and control physical systems which can move purposefully and without human intervention in real-world environments which have not been specifically engineered for the robot. The development of techniques for autonomous mobile robot operation constitutes one of the major trends in the current research and practice in modern robotics. This volume presents a variety of fuzzy logic techniques which address the challenges posed by autonomous robot navigation. The focus is on four major problems: how to design robust behavior-producing control modules; how to use data from sensors for the purpose of environment modeling; and how to integrate high-level reasoning and low-level behavior execution. In this volume state-of-the-art fuzzy logic solutions are presented and their pros and cons are discussed in detail based on extensive experimentation on real mobile robots.