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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/75381

    题名: Adaptive Control for Mimo Uncertain Nonlinear Systems Using Recurrent Wavelet Neural Network
    作者: Lin, Chih-Min;Ting, Ang-Bung;Hsu, Chun-Fei;Chung, Chao-Ming
    贡献者: 淡江大學電機工程學系
    关键词: Wavelet neural network;adaptive control;nonlinear system;uniformly ultimately bounded
    日期: 2012-02-01
    上传时间: 2012-03-22 14:36:28 (UTC+8)
    出版者: Singapore: World Scientific Publishing Co. Pte. Ltd.
    摘要: 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.
    關聯: International Journal of Neural Systems 22(1), pp.37-50
    DOI: 10.1142/S0129065712002992
    显示于类别:[電機工程學系暨研究所] 期刊論文


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