Fuzzy Logic, Identification and Predictive Control / Edition 1by Jairo Jose Espinosa Oviedo, Joos P.L. Vandewalle, Vincent Wertz
Pub. Date: 12/13/2010
Publisher: Springer London
The complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control prools. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses, circumstances where an imprecise linguistic description is often more relevant than a cut-and-dried… See more details below
The complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control prools. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses, circumstances where an imprecise linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious in this form of expert control system.
Divided into two parts, Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real-world industrial systems and simulations. The second part demonstrates the exploitation of such models to design control systems employing techniques like data mining.
Fuzzy Logic, Identification and Predictive Control is a comprehensive introduction to the use of fuzzy methods in many different control paradigms encompassing robust, model-based, PID-like and predictive control. This combination of fuzzy control theory and industrial serviceability will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Table of Contents
Part I: Fuzzy Modelling Fuzzy Modeling Constructing Fuzzy Models from Input-Output Data Fuzzy Modelling with Linguistic Integrity: A Tool for Data Mining Nonlinear Identification Using Fuzzy Models Part II: Fuzzy Control Fuzzy Control Predictive Control Based on Fuzzy Models Robust Nonlinear Predictive Control Using Fuzzy Models Conclusions and Future Perspectives Part III: Appendices A. Fuzzy Set Theory B. Clustering Methods C. Gradients Used in Identification with Fuzzy Models D. Discrete Linear Dynamic Approximation Theorem E. Fuzzy Control for a CVT
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