Adaptive backstepping Elman-based neural control for unknown nonlinear systems

doi:10.1016/j.neucom.2014.01.015

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題名:	Adaptive backstepping Elman-based neural control for unknown nonlinear systems
作者:	Hsu, Chun-Fei
貢獻者:	淡江大學電機工程學系
關鍵詞:	Neural controlChaotic systemInverted pendulumElman neural networkSelf-organizing neural network
日期:	2014-07-20
上傳時間:	2014-08-11 15:56:51 (UTC+8)
出版者:	Amsterdam: Elsevier BV
摘要:	This paper proposes an Elman-based self-organizing RBF neural network (ESRNN) which is a recurrent multilayered neural network, thus the ESRNN can handle the dynamic response. The ESRNN starts without any hidden neurons and all the hidden neurons are generated and learning online through a simultaneous structure and parameter learning via the Mahalanobis distance approach. Furthermore, an adaptive backstepping Elman-based neural control (ABENC) system which is composed of a computation controller and a switching controller is proposed. In this approach, the ESRNN is used to online approximate the unknown nonlinear system dynamics based on a Lyapunov function, so that system stability can be guaranteed. The switching controller is designed to eliminate the effect of the approximation error introduced by the ESRNN upon system stability. Finally, to effectively demonstrate the effectiveness of the proposed ABENC scheme, a chaotic system and an inverted pendulum are applied as example studies. The simulation results demonstrate that the proposed ABENC system can achieve favorable control performance after the structure and parameter learning of the ESRNN.
關聯:	Neurocomputing 136, p.170–179
DOI:	10.1016/j.neucom.2014.01.015
顯示於類別:	[電機工程學系暨研究所] 期刊論文

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