Fuzzy Modeling for Control

Fuzzy Modeling for Control

by Robert Babuska
     
 

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Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling of partly-known nonlinear systems. Fuzzy models can effectively integrate information from different sources, such as physical laws, empirical models, measurements and heuristics. Application areas of fuzzy models include prediction, decision support, system analysis, control design… See more details below

Overview

Rule-based fuzzy modeling has been recognised as a powerful technique for the modeling of partly-known nonlinear systems. Fuzzy models can effectively integrate information from different sources, such as physical laws, empirical models, measurements and heuristics. Application areas of fuzzy models include prediction, decision support, system analysis, control design, etc. Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering points of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models.
To automatically generate fuzzy models from measurements, a comprehensive methodology is developed which employs fuzzy clustering techniques to partition the available data into subsets characterized by locally linear behaviour. The relationships between the presented identification method and linear regression are exploited, allowing for the combination of fuzzy logic techniques with standard system identification tools. Attention is paid to the trade-off between the accuracy and transparency of the obtained fuzzy models. Control design based on a fuzzy model of a nonlinear dynamic process is addressed, using the concepts of model-based predictive control and internal model control with an inverted fuzzy model. To this end, methods to exactly invert specific types of fuzzy models are presented. In the context of predictive control, branch-and-bound optimization is applied.
The main features of the presented techniques are illustrated by means of simple examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.

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Editorial Reviews

Booknews
Babuska's (control engineering, Delft U. of Technology, the Netherlands) strategy is to develop transparent rule-based fuzzy models that can accurately predict the quantities of interest and at the same time provide insight into the system that generated the data. He highlights the selection of appropriate model structures in terms of the dynamic properties, as well as the internal structure of the fuzzy rules<-->linguistic, relational, or Takagi-Sugeno type. His methodology employees fuzzy clustering techniques to partition the available data into subsets characterized by linear behavior, then exploits the relationships between the presented identification method and linear regression to combine fuzzy logic techniques with standard tools for identifying systems. Annotation c. by Book News, Inc., Portland, Or.

Product Details

ISBN-13:
9789401060400
Publisher:
Springer Netherlands
Publication date:
04/30/2013
Series:
International Series in Intelligent Technologies, #12
Edition description:
Softcover reprint of the original 1st ed. 1998
Pages:
260
Product dimensions:
6.14(w) x 9.21(h) x 0.59(d)

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