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Dynamic event-triggered adaptive control for uncertain stochastic nonlinear systems

Author

Listed:
  • Fu, Yingying
  • Li, Jing
  • Li, Xiaobo
  • Wu, Shuiyan

Abstract

In this paper, the dynamic event-triggered adaptive tracking control problem is investigated via backstepping technology for uncertain stochastic nonlinear systems. First, the stochastic nonlinear system with unknown parameter is considered. By introducing an additional dynamic variable, a dynamic event-triggered adaptive controller is designed such that the closed-loop signals are uniformly ultimately bounded in the sense of the fourth moment. Then, a more general partially unknown stochastic nonlinear system is further considered, and the designed adaptive neural network control scheme ensures that the closed-loop signals are fourth moment semi-globally uniformly ultimately bounded (SGUUB). The proposed dynamic event-triggering mechanism (DETM) guarantees that the lengths of time intervals between each two consecutive events are lower-bounded by a positive constant. It is necessary to point out that the DETM is better at saving resources than the static event-triggering mechanism (SETM). Finally, two simulations are conducted to show the validity of the control strategies for these two systems, respectively.

Suggested Citation

  • Fu, Yingying & Li, Jing & Li, Xiaobo & Wu, Shuiyan, 2023. "Dynamic event-triggered adaptive control for uncertain stochastic nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    • Handle: RePEc:eee:apmaco:v:444:y:2023:i:c:s0096300322008682
    DOI: 10.1016/j.amc.2022.127800
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    References listed on IDEAS

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    1. Zhao, Nan-Nan & Wu, Li-Bing & Ouyang, Xin-Yu & Yan, Yan & Zhang, Rui-Yan, 2019. "Finite-time adaptive fuzzy tracking control for nonlinear systems with disturbances and dead-zone nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    2. Zhang, Yanhui & Liang, Hongjing & Ma, Hui & Zhou, Qi & Yu, Zhandong, 2018. "Distributed adaptive consensus tracking control for nonlinear multi-agent systems with state constraints," Applied Mathematics and Computation, Elsevier, vol. 326(C), pages 16-32.
    3. Wu, Jing & Sun, Wei & Su, Shun-Feng & Xia, Jianwei, 2022. "Neural-based adaptive control for nonlinear systems with quantized input and the output constraint," Applied Mathematics and Computation, Elsevier, vol. 413(C).
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