An Introduction to Fuzzy Control

1. Front Matter

   Pages I-XV

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2. Introduction

   • Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank

   Pages 1-36

3. The Mathematics of Fuzzy Control

   • Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank

   Pages 37-102

4. FKBC Design Parameters

   • Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank

   Pages 103-144

5. Nonlinear Fuzzy Control

   • Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank

   Pages 145-195

6. Adaptive Fuzzy Control

   • Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank

   Pages 197-244

7. Stability of Fuzzy Control Systems

   • Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank

   Pages 245-292

8. Back Matter

   Pages 293-316

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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.

• 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
• complexity

• Siemens AG, ZFE ST SN4, München, Germany

  Dimiter Driankov, Hans Hellendoorn, Michael Reinfrank

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