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Fuzzy Control of Industrial Systems

Theory and Applications

  • Book
  • © 1998

Overview

Authors:
  1. Ian S. Shaw

    1. Industrial Electronic Technology Research Group, Rand Afrikaans University, Johannesburg, Republic of South Africa

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 457)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. What is an Intelligent System?

    • Ian S. Shaw
    Pages 1-3

  3. Modeling Plants and Processes for Control Systems

    • Ian S. Shaw
    Pages 5-19

  4. Introduction to Set Theory and Fuzzy Logic

    • Ian S. Shaw
    Pages 21-32

  5. Set Operations

    • Ian S. Shaw
    Pages 33-54

  6. Generic Structure of Fuzzy Controllers

    • Ian S. Shaw
    Pages 55-66

  7. Fuzzy Controllers

    • Ian S. Shaw
    Pages 67-96

  8. System Identification for Rule-Based Systems

    • Ian S. Shaw
    Pages 97-111

  9. Stability Analysis of Fuzzy Control Systems

    • Ian S. Shaw
    Pages 113-118

  10. Neurofuzzy Controllers

    • Ian S. Shaw
    Pages 119-134

  11. Practical Fuzzy Controller Development

    • Ian S. Shaw
    Pages 135-154

  12. Examples of Fuzzy Control

    • Ian S. Shaw
    Pages 155-166

  13. Fuzzy Model of Human Control Operator

    • Ian S. Shaw
    Pages 167-179

  14. Collaborative Intelligent Control Systems

    • Ian S. Shaw
    Pages 181-186

  15. Conclusions

    • Ian S. Shaw
    Pages 187-187

  16. Back Matter

    Pages 189-192
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Keywords

About this book

Fuzzy Control of Industrial Systems: Theory and Applications presents the basic theoretical framework of crisp and fuzzy set theory, relating these concepts to control engineering based on the analogy between the Laplace transfer function of linear systems and the fuzzy relation of a nonlinear fuzzy system. Included are generic aspects of fuzzy systems with an emphasis on the many degrees of freedom and its practical design implications, modeling and systems identification techniques based on fuzzy rules, parametrized rules and relational equations, and the principles of adaptive fuzzy and neurofuzzy systems. Practical design aspects of fuzzy controllers are covered by the detailed treatment of fuzzy and neurofuzzy software design tools with an emphasis on iterative fuzzy tuning, while novel stability limit testing methods and the definition and practical examples of the new concept of collaborative control systems are also given. In addition, case studies of successful applications in industrial automation, process control, electric power technology, electric traction, traffic engineering, wastewater treatment, manufacturing, mineral processing and automotive engineering are also presented, in order to assist industrial control systems engineers in recognizing situations when fuzzy and neurofuzzy would offer certain advantages over traditional methods, particularly in controlling highly nonlinear and time-variant plants and processes.

Authors and Affiliations

  • Industrial Electronic Technology Research Group, Rand Afrikaans University, Johannesburg, Republic of South Africa

    Ian S. Shaw

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