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 that formal techniques exist for the analysis of a fuzzy controller, and that fuzzy control can be implemented when no expert knowledge is available. Chapter 1 introduces two major classes of knowledge based systems for closed loop 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.
An Introduction to Fuzzy Control
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First Sentence:
A very general definition, which encompasses the majority of knowledge based systems (KBS) applications to problems in closed-loop control, may be as follows: A KBS for closed-loop control is a control system which enhances the performance, reliability, and robustness of control by incorporating knowledge which cannot be accommodated in the analytic model upon which the design of a control algorithm is based, and that is usually taken care of by manual modes of operation, or by other safety and ancillary logic mechanisms. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
loop transient step response, fuzzy process model, normalized phase plane, conicity criterion, conventional control element, overall control output, composition based inference, scaled inference, nonlinear transfer element, current process output, fuzzy control literature, represent linguistic values, control output variables, auxiliary subspace, knowledge based controller, using fuzzy control, fuzzy control theory, fuzzification module, expert control system, normalized domain, fuzzy set definitions, sliding mode control, process state variables, flow rate difference, fuzzy propositions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Rolls Royce, Domain Fig, Symbolic Meaning New!
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A very general definition, which encompasses the majority of knowledge based systems (KBS) applications to problems in closed-loop control, may be as follows: A KBS for closed-loop control is a control system which enhances the performance, reliability, and robustness of control by incorporating knowledge which cannot be accommodated in the analytic model upon which the design of a control algorithm is based, and that is usually taken care of by manual modes of operation, or by other safety and ancillary logic mechanisms. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
loop transient step response, fuzzy process model, normalized phase plane, conicity criterion, conventional control element, overall control output, composition based inference, scaled inference, nonlinear transfer element, current process output, fuzzy control literature, represent linguistic values, control output variables, auxiliary subspace, knowledge based controller, using fuzzy control, fuzzy control theory, fuzzification module, expert control system, normalized domain, fuzzy set definitions, sliding mode control, process state variables, flow rate difference, fuzzy propositions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Rolls Royce, Domain Fig, Symbolic Meaning New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 29
books:
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90
books
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- Fuzzy Reasoning and Its Applications (Computers and people series) by E.H. Mamdani on 4 pages
- Industrial Applications of Fuzzy Control by M. Sugeno in Back Matter (1), and Back Matter (2)
- Possibility Theory by Didier Dubois in Back Matter
- Neural Networks for Control by W. Thomas Miller III in Back Matter
- Stability and Robustness of Multivariable Feedback Systems (Signal Processing, Optimization, and Control) by Michael George Safonov in Back Matter
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- Fuzzy Logic Control: Advances in Applications by H. B. Verbruggen on 5 pages
- Advances in Soft Computing - AFSS 2002: 2002 AFSS International Conference on Fuzzy Systems. Calcutta, India, February 3-6, 2002. Proceedings (Lecture ... / Lecture Notes in Artificial Intelligence) by Nikhil R. Pal on page 68, page 155, and page 170
- Applied Evolutionary Algorithms in Java by Robert Ghanea-Hercock on page 97, Back Matter (1), and Back Matter (2)
- Fuzzy Logic and Applications: 5th International Workshop, WILF 2003, Naples, Italy, October 9-11, 2003, Revised Selected Papers (Lecture Notes in ... / Lecture Notes in Artificial Intelligence) by Vito Di Gesł on page 70, and page 88
- Multiple Model Approaches to Modelling and Control by R Murray-Smith on page 64, and page 99
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