教師著作

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Author: search.filters.author.Y.-H. LiSubject: search.filters.subject.Adaptive Control

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    A GA-based indirect adaptive fuzzy-neural controller for uncertain nonlinear systems
    (2002-12-06) W.-Y. Wang; C.-C. Hsu; C.-W. Tao; Y.-H. Li
    In this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the RGA. Moreover, we propose an application of the RGA in designing an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear dynamical systems. The free parameters of the indirect adaptive fuzzy-neural controller can successfully be tuned on-line via the RGA approach. A supervisory controller is incorporated into the RIAFC to stabilize the closed-loop nonlinear system. An example of a nonlinear system controlled by RIAFC are demonstrated to show the effectiveness of the proposed method.