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Adaptive neural network asymptotical tracking control for an uncertain nonlinear system with input quantisation

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  • Haibin Sun
  • Linlin Hou
  • Guangdeng Zong

Abstract

In this paper, the problem of adaptive neural network asymptotical tracking is investigated for a class of nonlinear system with unknown function, external disturbances and input quantisation. Based on neural network technique, an adaptive asymptotical tracking controller is provided for an uncertain nonlinear system via backstepping method. In order to reduce complexity of the control algorithm in the backstepping design process, a sliding mode differentiator is employed to estimate the virtual control law and only two parameters need to be estimated via adaptive control technique. The stability of the closed-loop system is analysed by using Lyapunov function method and zero-tracking error performance is obtained in the presence of unknown nonlinear function, external disturbances and input quantisation. Finally, an application example is employed to demonstrate the effectiveness of the proposed scheme.

Suggested Citation

  • Haibin Sun & Linlin Hou & Guangdeng Zong, 2018. "Adaptive neural network asymptotical tracking control for an uncertain nonlinear system with input quantisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(9), pages 1974-1984, July.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:9:p:1974-1984
    DOI: 10.1080/00207721.2018.1481240
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    Cited by:

    1. Aravinth, N. & Sakthivel, R. & Satheesh, T. & Chadli, M., 2023. "Disturbance suppression based quantized tracking control for periodic piecewise polynomial systems," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

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