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Observer-based dynamic event-triggered control for nonstrict-feedback stochastic nonlinear multiagent systems

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  • Zhao, Guangtong
  • Cao, Liang
  • Li, Xiaomeng
  • Zhou, Qi

Abstract

In this paper, the consensus tracking control problem is investigated for stochastic nonlinear multiagent systems with nonstrict-feedback structure and unmeasurable states. Firstly, by proposing an event-triggered estimator, all followers not only can estimate the signal of leader, but also can avoid the continuous communication between agents. Then, an observer is designed to estimate the unmeasurable states. Based on backstepping and variable separation approach, an adaptive neural control strategy is proposed to deal with nonstrict-feedback nonlinearities. Furthermore, a modified dynamic event-triggered mechanism is developed to reduce the number of controller executions. Based on barrier Lyapunov function, an adaptive neural compensation control scheme is designed to tackle the input saturation and the state constraints of multiagent systems. Finally, it is confirmed that the proposed event-triggered adaptive controller can guarantee the convergence of consensus tracking errors and the semiglobal boundedness of all signals in the closed-loop system.

Suggested Citation

  • Zhao, Guangtong & Cao, Liang & Li, Xiaomeng & Zhou, Qi, 2022. "Observer-based dynamic event-triggered control for nonstrict-feedback stochastic nonlinear multiagent systems," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    • Handle: RePEc:eee:apmaco:v:430:y:2022:i:c:s0096300322003630
    DOI: 10.1016/j.amc.2022.127289
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    References listed on IDEAS

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    1. Hussain, Muntazir & Rehan, Muhammad & Ahmed, Shakeel & Abbas, Tanveer & Tufail, Muhammad, 2020. "A novel approach for static anti-windup compensation of one-sided Lipschitz systems under input saturation," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    2. Wang, Yanqian & Chen, Fu & Zhuang, Guangming & Yang, Guang, 2020. "Dynamic event-based mixed H∞ and dissipative asynchronous control for Markov jump singularly perturbed systems," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    3. Guo, Xiyue & Liang, Hongjing & Pan, Yingnan, 2020. "Observer-Based Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Multi-Agent Systems with Dead-Zone Input," Applied Mathematics and Computation, Elsevier, vol. 379(C).
    4. Ma, Jiali & Park, Ju H. & Xu, Shengyuan & Cui, Guozeng & Yang, Zhichun, 2020. "Command-filter-based adaptive tracking control for nonlinear systems with unknown input quantization and mismatching disturbances," Applied Mathematics and Computation, Elsevier, vol. 377(C).
    5. Sader, Malika & Chen, Zengqiang & Liu, Zhongxin & Deng, Chao, 2021. "Distributed robust fault-tolerant consensus control for a class of nonlinear multi-agent systems with intermittent communications," Applied Mathematics and Computation, Elsevier, vol. 403(C).
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    Cited by:

    1. Yi, Jiale, 2023. "Adaptive fuzzy connectivity-preserving consensus protocols for stochastic strict-feedback nonlinear MASs subject to unmeasured periodic disturbances," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    2. Tang, Tianfeng & Qin, Gang & Zhang, Bin & Cheng, Jun & Cao, Jinde, 2024. "Event-based asynchronous state estimation for Markov jump memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 473(C).
    3. Wenqiang Wu & Jiarui Liu & Fangyi Li & Yuanqing Zhang & Zikai Hu, 2023. "Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone," Mathematics, MDPI, vol. 11(4), pages 1-21, February.

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