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Tracking control problem of nonlinear strict-feedback systems with input nonlinearity: An adaptive neural network dynamic surface control method

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  • Minglong Zhou
  • Xiyu Zhang
  • Xiongfeng Deng

Abstract

In this work, the tracking control problem for a class of nonlinear strict-feedback systems with input nonlinearity is addressed. In response to the influence of input nonlinearity, an auxiliary control system is constructed to compensate for it. To process unknown nonlinear dynamics, radial basis function neural networks (RBFNNs) are introduced to approximate them, and some adaptive updating control laws are designed to estimate unknown parameters. Furthermore, during the dynamic surface control (DSC) design process, first-order low-pass filters are introduced to solve the complexity explosion problems caused by repeated differentiation. After that, an NN-based adaptive dynamic surface tracking controller is proposed to achieve the tracking control. By applying the proposed controller, it can be guaranteed that not only the output of the system can track the desired trajectory, but also that the tracking error can converge to a small neighborhood of zero, while all signals of the closed-loop system are bounded. Finally, the effectiveness of the proposed controller is verified through two examples.

Suggested Citation

  • Minglong Zhou & Xiyu Zhang & Xiongfeng Deng, 2024. "Tracking control problem of nonlinear strict-feedback systems with input nonlinearity: An adaptive neural network dynamic surface control method," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-22, October.
    • Handle: RePEc:plo:pone00:0312345
    DOI: 10.1371/journal.pone.0312345
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    References listed on IDEAS

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    1. Liang Liu & Mengru Kong, 2023. "Exponential output feedback control for a class of strict-feedback nonlinear systems with different time-delay states," International Journal of Systems Science, Taylor & Francis Journals, vol. 54(7), pages 1503-1512, May.
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