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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/80337

    Title: Intelligent tracking control of a DC motor driver using self-organizing TSK type fuzzy neural networks
    Authors: Hsu, Chun-Fei
    Contributors: 淡江大學電機工程學系
    Keywords: Adaptive control;Neural control;Fuzzy neural network;Self-organizing;DC motor driver
    Date: 2011-04-06
    Issue Date: 2013-01-22 18:39:37 (UTC+8)
    Publisher: Dordrecht: Springer Netherlands
    Abstract: In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then, an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller uses an STFNN to approximate an ideal controller, and the robust compensator is designed to eliminate the approximation error in the Lyapunov stability sense without occurring chattering phenomena. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived to speed up the convergence rates of the tracking error. Finally, the proposed ASTFNC system is applied to a DC motor driver on a field-programmable gate array chip for low-cost and high-performance industrial applications. The experimental results verify the system stabilization and favorable tracking performance, and no chattering phenomena can be achieved by the proposed ASTFNC scheme.
    Relation: Nonlinear Dynamics 67(1), pp.587-600
    DOI: 10.1007/s11071-011-0012-8
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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