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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/75381

    Title: Adaptive Control for Mimo Uncertain Nonlinear Systems Using Recurrent Wavelet Neural Network
    Authors: Lin, Chih-Min;Ting, Ang-Bung;Hsu, Chun-Fei;Chung, Chao-Ming
    Contributors: 淡江大學電機工程學系
    Keywords: Wavelet neural network;adaptive control;nonlinear system;uniformly ultimately bounded
    Date: 2012-02-01
    Issue Date: 2012-03-22 14:36:28 (UTC+8)
    Publisher: Singapore: World Scientific Publishing Co. Pte. Ltd.
    Abstract: Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, good generalization capability and information storing ability. With these advantages, this paper proposes an RWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The RBAC system is composed of a neural controller and a bounding compensator. The neural controller uses an RWNN to online mimic an ideal controller, and the bounding compensator can provide smooth and chattering-free stability compensation. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. Finally, the proposed RBAC system is applied to the MIMO uncertain nonlinear systems such as a mass-spring-damper mechanical system and a two-link robotic manipulator system. Simulation results verify that the proposed RBAC system can achieve favorable tracking performance with desired robustness without any chattering phenomenon in the control effort.
    Relation: International Journal of Neural Systems 22(1), pp.37-50
    DOI: 10.1142/S0129065712002992
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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