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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/91650


    Title: Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network
    Authors: Hsu, Chun-Fei
    Contributors: 淡江大學電機工程學系
    Keywords: Adaptive control;Neural control;Functional-linked neural network;Wavelet neural network
    Date: 2013-04-01
    Issue Date: 2013-07-23 21:45:07 (UTC+8)
    Publisher: Kidlington: Pergamon
    Abstract: Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.
    Relation: Engineering Applications of Artificial Intelligence 26(4), pp.1221–1229
    DOI: 10.1016/j.engappai.2012.11.012
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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