Fuzzy Control Of Industrial Systems
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Fuzzy Control of Industrial Systems
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.
An Introduction to Fuzzy Control
Author: Dimiter Driankov
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-29
Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as integral parts of a wide range of industrial process control systems and consumer products. Applications of fuzzy controllers are an established practice for Japanese manufacturers, and are spreading in Europe and America. 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. The book is mainly oriented to control engineers and theorists, although parts can be read without any knowledge of control theory and may interest AI people. This 2nd, revised edition incorporates suggestions from numerous reviewers and updates and reorganizes some of the material.
Advanced Fuzzy Logic Technologies in Industrial Applications
Author: Ying Bai
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-01-17
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. In the mid-1960s and contemporary with Kalman’s pioneering papers on sta- space models and optimal control, L.A. Zadeh began publishing papers on “fuzzy sets”. It took another decade before the fuzzy-logic controller due to Mamdani and Assilion was reported in the literature (ca. 1974), and now the fuzzy-logic control paradigm is entering its fifth decade of development and application. Thus, this new Advances in Industrial Control monograph edited by Ying Bai, Hanqi Zhuang and Dali Wang on fuzzy-logic control and its practical application comes as a timely reminder of the wide range of problems that can be solved by this continually evolving methodology.