Adaptive Control for Mimo Uncertain Nonlinear Systems Using Recurrent Wavelet Neural Network

doi:10.1142/S0129065712002992

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/75381

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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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