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Adaptive fuzzy event-triggered control for nonstrict-feedback switched stochastic nonlinear systems with state constraints

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  • Yongchao Liu
  • Qidan Zhu

Abstract

Due to the frequent occurrence of random disturbances in practical physical systems, the control design of stochastic systems has a significant application background. This article presents an event-triggered adaptive fuzzy control scheme for nonstrict-feedback switched stochastic nonlinear systems with state constraints. The barrier Lyapunov functions are deployed to make all states maintain the prescribed regions. In addition, the event-triggered mechanism is incorporated into the backstepping framework to mitigate the data transmission. The fuzzy logic systems are exploited to cope with the system uncertainties, and then the adaptive fuzzy control strategy is recursively constructed. The devised event-triggered adaptive fuzzy controller can not only surmount the influence of state constraints but also decrease unnecessary resource consumption. In virtue of common Lyapunov function method, it is shown that all system signals are bounded under switching signals and the predefined constraints are not violated. Finally, the validity of the presented scheme is elucidated by simulation results.

Suggested Citation

  • Yongchao Liu & Qidan Zhu, 2021. "Adaptive fuzzy event-triggered control for nonstrict-feedback switched stochastic nonlinear systems with state constraints," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(14), pages 2889-2903, October.
    • Handle: RePEc:taf:tsysxx:v:52:y:2021:i:14:p:2889-2903
    DOI: 10.1080/00207721.2021.1910878
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    Cited by:

    1. Xiongfeng Deng & Yiming Yuan & Lisheng Wei & Binzi Xu & Liang Tao, 2022. "Adaptive Neural Tracking Control for Nonstrict-Feedback Nonlinear Systems with Unknown Control Gains via Dynamic Surface Control Method," Mathematics, MDPI, vol. 10(14), pages 1-13, July.

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