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Robust adaptive neural control for a class of uncertain MIMO nonlinear systems

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  • Chenliang Wang
  • Yan Lin

Abstract

In this paper, a novel robust adaptive neural control scheme is proposed for a class of uncertain multi-input multi-output nonlinear systems. The proposed scheme has the following main features: (1) a kind of Hurwitz condition is introduced to handle the state-dependent control gain matrix and some assumptions in existing schemes are relaxed; (2) by introducing a novel matrix normalisation technique, it is shown that all bound restrictions imposed on the control gain matrix in existing schemes can be removed; (3) the singularity problem is avoided without any extra effort, which makes the control law quite simple. Besides, with the aid of the minimal learning parameter technique, only one parameter needs to be updated online regardless of the system input–output dimension and the number of neural network nodes. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

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  • Chenliang Wang & Yan Lin, 2015. "Robust adaptive neural control for a class of uncertain MIMO nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(11), pages 1934-1943, August.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:11:p:1934-1943
    DOI: 10.1080/00207721.2013.843214
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    Cited by:

    1. Yao, Dajie & Dou, Chunxia & Xie, Xiangpeng & Hu, Songlin, 2022. "Containment control of non-affine multi-agent systems based on given precision," Applied Mathematics and Computation, Elsevier, vol. 412(C).

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