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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/91649


    Title: Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems
    Authors: Hsu, Chun-Fei;Lin, Chih-Min;Yeh, Rong-Guan
    Contributors: 淡江大學電機工程學系
    Keywords: Adaptive control;Sliding-mode control;Neural-fuzzy system;Chaotic system;Inverted pendulum
    Date: 2013-04-01
    Issue Date: 2013-07-23 21:42:58 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: Many published papers show that a TSK-type fuzzy system provides more powerful representation than a Mamdani-type fuzzy system. Radial basis function (RBF) network has a similar feature to the fuzzy system. As this result, this article proposes a dynamic TSK-type RBF-based neural-fuzzy (DTRN) system, in which the learning algorithm not only online generates and prunes the fuzzy rules but also online adjusts the parameters. Then, a supervisory adaptive dynamic RBF-based neural-fuzzy control (SADRNC) system which is composed of a DTRN controller and a supervisory compensator is proposed. The DTRN controller is designed to online estimate an ideal controller based on the gradient descent method, and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the DTRN controller upon the system stability in the Lyapunov sense. Finally, the proposed SADRNC system is applied to control a chaotic system and an inverted pendulum to illustrate its effectiveness. The stability of the proposed SADRNC scheme is proved analytically and its effectiveness has been shown through some simulations.
    Relation: Applied Soft Computing 13(4), pp.1620-1626
    DOI: 10.1016/j.asoc.2012.12.028
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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