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Adaptive neural prescribed performance control for a class of strict-feedback stochastic nonlinear systems under arbitrary switchings

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  • Wenjie Si
  • Xunde Dong
  • Feifei Yang

Abstract

This paper presents an adaptive neural tracking control scheme for strict-feedback stochastic nonlinear systems with guaranteed transient and steady-state performance under arbitrary switchings. First, by utilising the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, radial basis function neural networks approximation are used to handle unknown nonlinear functions and stochastic disturbances. At last, by using the common Lyapunov function method and the backstepping technique, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterisation, and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded, and the prescribed tracking control performance are guaranteed under arbitrary switchings. Three examples are presented to further illustrate the effectiveness of the proposed approach.

Suggested Citation

  • Wenjie Si & Xunde Dong & Feifei Yang, 2017. "Adaptive neural prescribed performance control for a class of strict-feedback stochastic nonlinear systems under arbitrary switchings," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(11), pages 2300-2310, August.
  • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:11:p:2300-2310
    DOI: 10.1080/00207721.2017.1316880
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    Cited by:

    1. Xiaohuan Lai & Haipeng Pan & Xinlong Zhao, 2019. "Adaptive Control for Pure-Feedback Nonlinear Systems Preceded by Asymmetric Hysteresis," Energies, MDPI, vol. 12(24), pages 1-13, December.
    2. Wu, Ziwen & Zhang, Tianping & Xia, Xiaonan & Hua, Yu, 2022. "Finite-time adaptive neural command filtered control for non-strict feedback uncertain multi-agent systems including prescribed performance and input nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 421(C).

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