Adaptive dynamic RBF neural controller design for a class of nonlinear systems

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 987654321/73594

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题名:	Adaptive dynamic RBF neural controller design for a class of nonlinear systems
作者:	Hsu, Chun-Fei
贡献者:	淡江大學電機工程學系
关键词:	Adaptive control;Neural control;Lyapunov stability theorem;DC motor;Chaotic system
日期:	2011-12
上传时间:	2011-11-29 19:26:17 (UTC+8)
出版者:	Amsterdam: Elsevier BV
摘要:	In this paper, an adaptive DRBF neural control (ADNC) system which is composed of a neural controller and a smooth compensator is proposed. The neural controller utilizes a dynamic radial basis function (DRBF) network to online mimic an ideal controller and the smooth compensator is designed to eliminate the effect of the approximation error between the ideal controller and neural controller. The DRBF network can self-organizing its network structure. All the controller parameters of the proposed ADNC system are online tuned in the Lyapunov sense, thus the stability analytic shows the system output can exponentially converge to a small neighborhood of the trajectory command. Finally, the proposed ADNC system is applied to a chaotic system and a DC motor. Simulation and experimental results verify that a favorable tracking performance and no chattering phenomena can be achieved by the proposed ADNC system.
關聯:	Applied Soft Computing 11(8), pp.4607–4613
DOI:	10.1016/j.asoc.2011.08.001
显示于类别:	[電機工程學系暨研究所] 期刊論文

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