An Introduction to Fuzzy Control

  • Dimiter Driankov
  • Hans Hellendoorn
  • Michael Reinfrank

Table of contents

  1. Front Matter
    Pages I-XV
  2. Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank
    Pages 1-36
  3. Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank
    Pages 37-102
  4. Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank
    Pages 103-144
  5. Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank
    Pages 145-195
  6. Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank
    Pages 197-244
  7. Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank
    Pages 245-292
  8. Back Matter
    Pages 293-316

About this book

Introduction

Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic to compute an appropriate control action. These fuzzy knowledge based controllers can be found either as stand-alone control elements or as integral parts of distributed control systems including conventional controllers in a wide range of industrial process control systems and consumer products. Applications of fuzzy controllers have become a well established practice for Japanese manufacturers of control equipment and systems, and are becoming more and more common for their European and American counterparts. 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. Thus the book is mainly oriented toward control engineers and theorists rather than fuzzy and non-fuzzy AI people. However, parts can be read without any knowledge of control theory and may be of interest to AI people. The book has six chapters. Chapter 1 introduces two major classes of knowledge based systems for closedloop control. Chapter 2 introduces relevant parts of fuzzy set theory and fuzzy logic. Chapter 3 introduces the principal design parameters of a fuzzy knowledge based controller (FKBC) and discusses their relevance with respect to its performance. Chapter 4 considers an FKBC as a particular type of nonlinear controller. Chapter 5 considers tuning and adaptation of FKBCs, which are nonlinear and so can be designed to cope with a certain amount of nonlinearity. Chapter 6 considers several approaches for stability analysis of FKBCs in the context of classical nonlinear dynamic systems theory.

Keywords

artificial intelligence control control theory fuzzy control fuzzy controller fuzzy logic fuzzy set industrial process knowledge knowledge base linearity nonlinear control nonlinearity stability systems theory

Authors and affiliations

  • Dimiter Driankov
    • 1
  • Hans Hellendoorn
    • 1
  • Michael Reinfrank
    • 1
  1. 1.Siemens AG, ZFE ST SN4MünchenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-11131-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 1993
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-662-11133-8
  • Online ISBN 978-3-662-11131-4
  • About this book