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Adaptive neural output feedback tracking control for a class of nonlinear systems

Author

Listed:
  • Yu-Qun Han
  • Shan-Liang Zhu
  • De-Yu Duan
  • Shu-Guo Yang

Abstract

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.

Suggested Citation

  • Yu-Qun Han & Shan-Liang Zhu & De-Yu Duan & Shu-Guo Yang, 2019. "Adaptive neural output feedback tracking control for a class of nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(11), pages 2088-2101, August.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:11:p:2088-2101
    DOI: 10.1080/00207721.2019.1645918
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    Cited by:

    1. Cui, Di & Zou, Wencheng & Guo, Jian & Xiang, Zhengrong, 2022. "Neural network-based adaptive finite-time tracking control of switched nonlinear systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 428(C).

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