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An improved adaptive neural asymptotic tracking control for pure-feedback nonlinear systems with disturbances

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  • Huanqing Wang
  • Lingjia Zhao

Abstract

This note considers the issue of the asymptotic tracking control for a class of pure-feedback systems (PFSs) with disturbances. During the controller construction, the mean value theorem (MVT) is utilised to tackle the non-affine structure of the original systems. Then, we employ radial basis function neural networks (RBFNNs) to dispose the difficulty of the uncertain nonlinear functions. An improved Lyapunov function which relaxes the restrictions on design parameters is designed through introducing the lower bounds. By utilising the backstepping algorithm and the stability theory of Lyapunov function, an adaptive neural asymptotic tracking control strategy is designed. The control scheme can ensure all the states in the considered system are bounded and the tracking error inclines to zero asymptotically. Finally, simulation examples verify the validity of the designed strategy.

Suggested Citation

  • Huanqing Wang & Lingjia Zhao, 2025. "An improved adaptive neural asymptotic tracking control for pure-feedback nonlinear systems with disturbances," International Journal of Systems Science, Taylor & Francis Journals, vol. 56(15), pages 3571-3586, November.
    • Handle: RePEc:taf:tsysxx:v:56:y:2025:i:15:p:3571-3586
    DOI: 10.1080/00207721.2025.2471025
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