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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/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 control;Chaotic system;Inverted pendulum;Elman neural network;Self-organizing neural network
    Date: 2014-07
    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.
    由於類神經網路經由學習法則可以將類神經網路中的連結權重質做調整,藉以得到近似非線性函數所需的理想連結權重值,並且擁有對於雜訊和不確定干擾的強健抑制的能力。尤其,因為遞迴式類神經網路(recurrent neutral network)具有內部的迴授迴路,可以處理系統在沒有外在延遲迴授下的複雜動態響應,可以得知,遞迴式類神經網路是一種動態的映射結構。Elman類神經網路(Elman neutral network)是一種遞迴式類神經網路,因為其卓越的動態特性,現今已廣泛運用在動態系統的鑑別與控制上。
    幅狀基底函數(radial basis function, RBF)類神經網路,其特質主要在於模擬大腦皮質層軸突的局部調整功能,具備相當良好的映射能力,但不具有內部的迴授迴路,無法處理複雜的動態響應。本研究提出自構式Elman基底RBF類神經網路(Elman-based self-organizing RBF neutral network)。結合了自構式類神經網路之網路架構自我建構能力與Elman類神經網路具有動態近似非線性函數能力,故自構式Elman基底RBF類神經網路的特點在於學習速度快、網路具穩定性與可塑性、執行時間短、網路收斂快,非常適合應用於非線性動態控制領域上。
    步階迴歸控制(backstepping control)設計為有效的非線性強健控制法則之一,其利用系統狀態變數來分別設計所相對應的控制子法則,再透過此一連串重複且遞迴式的設計,最後推導獲得整個受控系統的最終控制法則。本研究提出了適應性步階迴歸Elman基底類神經控制器(adaptive backstepping Elman-based neutral control),其利用所提出的自構式Elman基底RBF類神經網路線上即時學習近似受控系統的不確定量以及外來擾動項,可達到抵抗參數變化和外力干擾的功效。穩定度分析方面,經由重複遞迴式的選取李亞普諾夫函數,加以證明整個系統的穩定性,並且由於系統狀態變數常被引入設計過程中,因此藝能保證系統內部動態的穩定性。最後,將所提出的適應性步階迴歸Elman基底類神經控制系統運用於混沌動態控制與倒單擺平衡控制上,經由模擬結果充分顯示所提出之設計方法可以獲得良好的控制響應,甚至當系統有參數變化與外力干擾時,依舊可以獲得不錯的控制成果。
    Relation: Neurocomputing 136, pp.170–179
    DOI: 10.1016/j.neucom.2014.01.015
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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