淡江大學機構典藏:Item 987654321/98519
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/98519


    Title: Adaptive backstepping Elman-based neural control for unknown nonlinear systems
    Authors: Hsu, Chun-Fei
    Contributors: 淡江大學電機工程學系
    Keywords: Neural controlChaotic systemInverted pendulumElman neural networkSelf-organizing neural network
    Date: 2014-07-20
    Issue Date: 2014-08-11 15:56:51 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: 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.
    Relation: Neurocomputing 136, p.170–179
    DOI: 10.1016/j.neucom.2014.01.015
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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