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Adaptive fuzzy dynamic surface control for nonstrict-feedback nonlinear state constrained systems with input dead-zone via event-triggered sampling

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  • Hu, Yifan
  • Liu, Wenhui

Abstract

In this paper, a novel adaptive fuzzy dynamic surface control (DSC) via the event-triggered mechanism is developed for nonstrict-feedback nonlinear state constrained systems preceded by actuator dead-zone. The utilization of the dynamic surface technique avoids the issue of repeated differentiation of virtual control law and eliminates the drawback of the explosion of complexity with the backstepping technique. By utilizing the universal approximation property of fuzzy logic systems (FLSs), the unknown function is approximated successfully. By combining the DSC and event-triggered control (ETC) scheme, communication and computation strain are alleviated contemporaneously. An adaptive controller and an auxiliary system are cooperatively constructed to offset the effect of dead-zone nonlinear function. By employing the barrier Lyapunov functions (BLFs), all the states can remain within a prescribed interval. The tracking error can be restricted at the origin and all signals of closed-loop systems can be kept within the boundaries by the designed parameter adaptive laws and event-triggered controller. Finally, a numerical simulation result and a practical simulation result of the pendulum are proposed to guarantee the effectiveness of the presented control theory.

Suggested Citation

  • Hu, Yifan & Liu, Wenhui, 2023. "Adaptive fuzzy dynamic surface control for nonstrict-feedback nonlinear state constrained systems with input dead-zone via event-triggered sampling," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    • Handle: RePEc:eee:apmaco:v:450:y:2023:i:c:s0096300323001546
    DOI: 10.1016/j.amc.2023.127985
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    References listed on IDEAS

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    1. Chen, Qi-Xin & Chang, Xiao-Heng, 2022. "Resilient filter of nonlinear network systems with dynamic event-triggered mechanism and hybrid cyber attack," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    2. Jin-Zi Yang & Yuan-Xin Li, 2020. "Fuzzy adaptive asymptotic tracking of uncertain nonlinear systems with full states constraints," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(16), pages 3550-3562, December.
    3. 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.
    4. Wu, Li-Bing & Park, Ju H. & Xie, Xiang-Peng & Liu, Ya-Juan & Yang, Zhi-Chun, 2020. "Event-triggered adaptive asymptotic tracking control of uncertain nonlinear systems with unknown dead-zone constraints," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    5. Yu-Han Hu & Ling Zhao & Li-Bing Wu & Nan-Nan Zhao & Yu-Jun Zhang, 2020. "Adaptive event-triggered fuzzy tracking control of nonlinear systems with dead-zones and unmeasurable states," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(16), pages 3251-3268, December.
    6. Qing-Yuan Xu & Xiao-Dong Li, 2018. "Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(9), pages 1878-1894, July.
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    Cited by:

    1. Wang, Huanqing & Meng, Zhu, 2024. "Fixed-time adaptive neural tracking control for high-order nonlinear switched systems with input saturation and dead-zone," Applied Mathematics and Computation, Elsevier, vol. 480(C).

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