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

Sung-Kwun Oh, Seok-Beom Rho, and Hyun-Ki Kim
International Journal of Control, Automation, and Systems, vol. 2, no. 3, pp.362-373, 2004

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.

Keyword : Computational Intelligence (CI), estimation algorithm, fuzzy controller, genetic algorithms, neurofuzzy networks (NFN), optimization process, scaling factors

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