Shaw Fuzzy Control of Industrial Systems

Theory and Applications
Erscheinungsjahr 2013
ISBN: 978-1-4757-2813-2
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 457, 192 Seiten, eBook

Reihe: The Springer International Series in Engineering and Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Shaw, Ian S.

Inhaltsverzeichnis

Notes to Instructors. Figures. Tables. 1. What is an Intelligent System? 2. Modeling Plants and Processes of Control Systems. 3. Introduction to Set Theory and Fuzzy Logic. 4. Set Operations. 5. Generic Structure of Fuzzy Controllers. 6. Fuzzy Controllers. 7. System Identification for Rule-Based Systems. 8. Stability Analysis of Fuzzy Control System. 9. Neurofuzzy Controllers. 10. Practical Fuzzy Controller Development. 11. Examples of Fuzzy Control. 12. Fuzzy Model of Human Control Operator. 13. Collaborative Intelligent Control Systems. 14. Conclusions. Index.