Model based fuzzy control uses a given conventional or fuzzy open loop model of the plant under control to derive the set of fuzzy if-then rules for the fuzzy controller. Of central interest are the stability, performance, and robustness properties of the resulting closed loop system involving a conventional or fuzzy model and a fuzzy controller. 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 properties superior to 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. A comprehensive and up-to-date treatment of model based fuzzy control and its relationship to conventional control, the text is intended to serve as a guide for scientists and practitioners and to provide introductory material on fuzzy control 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. Furthermore, of central interest are the consequent stability, performance, and robustness analysis of the resulting closed loop system involving a conventional model and a fuzzy controller, or a fuzzy model and a fuzzy controller. The major objective of the model-based fuzzy control is to use the full available range of existing linear and nonlinear design of such fuzzy controllers which have better stability, performance, and robustness properties than the corresponding non-fuzzy controllers designed by the use of these same techniques.

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 book contains a selection of revised papers and state-of-the-art overviews on current trends and future perspectives of fuzzy systems. A major aim is to address theoretical as well as application-oriented issues and to contribute to the foundation of concepts, methods, and tools in this field. The book is written by researchers who attended the workshop "Fuzzy Systems '93 - Management of Uncertain Information" (Braunschweig, Germany, October 21-22, 1993), organized by the German Society of Computer Science (GI), the German Computer Science Academy (DIA), and the University of Braunschweig.Dieses Buch enthalt ausgewahlte und auf neuesten Stand gebrachte Fachaufsatze und "State of the Art"-Ubersichtsartikel in englischer Sprache. Sie geben einen Uberblick uber aktuelle Trends sowie Zukunftsperspektiven der Fuzzy-Systeme. Besonderer Wert wird darauf gelegt, dass das Buch in einem ausgewogenen Verhaltnis von Theorie und Praxis zur Fundierung von Konzepten, Methoden und Werkzeugen beitragt. Hervorgegangen ist das Werk aus einem von der Gesellschaft fur Informatik (GI), der Deutschen Informatik Akademie (DIA) und der TU Braunschweig gemeinsam veranstalteten GI-Workshop "Fuzzy-Systeme '93 - Management unsicherer Informationen" (Braunschweig, 21.-22.10.1993). Die Aufsatze wurden uberarbeitet und um Uberblicksartikel erganzt, geschrieben von H. J. Zimmermann, H. Hellendorn, D. Nauck, C. Freksa, S. Gottwald und K. D. Meyer-Gramann.