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Event-triggered adaptive asymptotic tracking control of uncertain nonlinear systems with unknown dead-zone constraints

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  • Wu, Li-Bing
  • Park, Ju H.
  • Xie, Xiang-Peng
  • Liu, Ya-Juan
  • Yang, Zhi-Chun

Abstract

This paper studies the adaptive asymptotic tracking control problem of uncertain nonlinear systems with unknown control directions, dead-zone constraints and event-triggered inputs. By constructing the proper parameter updated laws with a positive continuous integrable function of augmented dimension vectors, unknown upper bound and compound disturbance, an improved adaptive backstepping control scheme is developed to substantially deal with the effect of unknown parameter vectors and dead-zone nonlinearities. Accordingly, an associated event-triggered mechanism including the decreasing function of tracking errors is designed to obtain the desired tracking performance and thus the computation burden of the transmissions procedure can be significantly alleviated. It is further shown that all the closed-loop signals are uniformly bounded and that the asymptotic tracking can be achieved based on Lyapunov function method. Finally, the validity of the presented approach is demonstrated by two simulation examples.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:386:y:2020:i:c:s0096300320304732
    DOI: 10.1016/j.amc.2020.125528
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    References listed on IDEAS

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    1. Xi, Changjiang & Zhai, Ding & Li, Xiaojian & Zhang, Qingling, 2017. "Decentralized adaptive delay-dependent neural network control for a class of large-scale interconnected nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 148-163.
    2. Wu, Li-Bing & Wang, Heng & He, Xi-Qin & Zhang, Da-Qing, 2018. "Decentralized adaptive fuzzy tracking control for a class of uncertain large-scale systems with actuator nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 390-405.
    3. Hongcheng Zhou & Dezhi Xu & Daobo Wang & Le Ge, 2014. "Adaptive Fault-Tolerant Tracking Control of Nonaffine Nonlinear Systems with Actuator Failure," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-8, October.
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    Cited by:

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    2. Yoo, Sung Jin, 2021. "Decentralized event-triggered adaptive control of a class of uncertain interconnected nonlinear systems using local state feedback corrupted by unknown injection data," Applied Mathematics and Computation, Elsevier, vol. 399(C).
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    5. 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.
    6. Wang, Le & Sun, Wei & Su, Shun-Feng, 2022. "Adaptive asymptotic tracking control for nonlinear systems with state constraints and input saturation," Applied Mathematics and Computation, Elsevier, vol. 431(C).
    7. Ju, Xinxu & Jia, Xianglei & Shi, Xiaocheng & Yu, Shan’en, 2022. "Adaptive output feedback event-triggered tracking control for nonlinear systems with unknown control coefficient," Applied Mathematics and Computation, Elsevier, vol. 432(C).
    8. Liu, Shanlin & Niu, Ben & Zong, Guangdeng & Zhao, Xudong & Xu, Ning, 2022. "Adaptive fixed-time hierarchical sliding mode control for switched under-actuated systems with dead-zone constraints via event-triggered strategy," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    9. Hu, Yifan & Liu, Wenhui & Liu, Guobao, 2022. "Adaptive finite‐time event‐triggered control for uncertain nonlinearly parameterized systems with unknown control direction and actuator failures," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    10. Yuan, Manman & Zhai, Junyong & Ye, Hui, 2022. "Adaptive output feedback control for a class of switched stochastic nonlinear systems via an event-triggered strategy," Applied Mathematics and Computation, Elsevier, vol. 422(C).

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