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Adaptive control for a class of nonlinear time-delay systems preceded by unknown hysteresis

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  • Xiuyu Zhang
  • Yan Lin

Abstract

In this article, a robust adaptive neural dynamic surface control is proposed for a class of time-delay nonlinear systems preceded by saturated hystereses. Compared with the present schemes of dealing with time delay and hystereses input, the main advantages of the proposed scheme are that the prespecified transient and steady-state performance of tracking error can be guaranteed, the computational burden can be greatly reduced and the explosion of complexity problem inherent in backstepping control can be eliminated. Moreover, the utilisation of saturated-type Prandtl–Ishlinskii model makes our scheme more applicable. It is proved that the new scheme can guarantee all the closed-loop signals semiglobally uniformly ultimate bounded. Simulation results are presented to demonstrate the validity of the proposed scheme.

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  • Xiuyu Zhang & Yan Lin, 2013. "Adaptive control for a class of nonlinear time-delay systems preceded by unknown hysteresis," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(8), pages 1468-1482.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:8:p:1468-1482
    DOI: 10.1080/00207721.2012.659690
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    Cited by:

    1. Yeong-Chan Chang, 2014. "Robust adaptive neural tracking control for a class of electrically driven robots with time delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(11), pages 2418-2434, November.
    2. Jilie Zhang & Huaguang Zhang & Binrui Wang & Tiaoyang Cai, 2016. "Nearly data-based optimal control for linear discrete model-free systems with delays via reinforcement learning," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(7), pages 1563-1573, May.

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