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Fuzzy Controller Design by Means of Genetic Optimization and NFN-Based Estimation Technique  

Oh, Sung-Kwun (Dept. of Electrical Electronic ＆ Information Engineering, Wonkwang University)
Park, Seok-Beom (Dept. of Electrical Electronic ＆ Information Engineering, Wonkwang University)
Kim, Hyun-Ki (Dept. of Electrical Engineering, University of Suwon)
Publication Information
International Journal of Control, Automation, and Systems / v.2, no.3, 2004 , pp. 362-373 More about this Journal
Abstract
In this study, we introduce a noble neurogenetic approach to the design of the fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and neurofuzzy networks (NFN). The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, tuning of the scaling factors of the fuzzy controller is carried out, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based NFN. The developed approach is applied to an inverted pendulum nonlinear system where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.
Keywords
Computational Intelligence (CI); estimation algorithm; fuzzy controller; genetic algorithms; neurofuzzy networks (NFN); optimization process; scaling factors;
Citations & Related Records

Times Cited By Web Of Science : 8  (Related Records In Web of Science)
Times Cited By SCOPUS : 10
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