教師著作

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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.
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    Adaptive fuzzy-neural sliding mode control for a class of uncertain nonlinear dynamical systems
    (2001-03-24) W.-Y. Wang; M.-L. Chan; T.-T. Lee
    In this paper, a novel design algorithm of adaptive fuzzy-neuralsliding mode control for a class of uncertain nonlinear dynamicalsystems is proposed to attenuate the effects caused by unmodeleddynamics, disturbances and approximate errors. Since fuzzy-neuralsystems can uniformly approximate nonlinear continuous functions toarbitrary accuracy, the adaptive fuzzy control theory is employed toderive the control law for a class of nonlinear system, with unknownnonlinear functions and disturbances. Moreover, the sliding modecontrol method is incorporated into the control law so that thederived controller is robust with respect to unmodeled dynamics,disturbances and approximate errors. To demonstrate the effectivenessof the proposed method, an example is illustrated in this paper.