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Finite-time adaptive neural command filtered control for non-strict feedback uncertain multi-agent systems including prescribed performance and input nonlinearities

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  • Wu, Ziwen
  • Zhang, Tianping
  • Xia, Xiaonan
  • Hua, Yu

Abstract

In this paper, the issue of finite-time consensus tracking control (CTC) is discussed for uncertain nonlinear multi-agent systems (MASs) with prescribed performance and input saturation. By constructing a finite-time performance function (FTPF), the tracking errors converge to a predefined attenuation range within finite-time. By using tanh(·) function and mathematical transformation, the effect of input saturation is solved. The unmodeled dynamics and dynamic disturbances of the system are solved by means of measurable dynamic signals. Through the Young’s inequality and the properties of Gaussian function, the coupling problem between multi-agents and the system controller design problem in non-strict feedback form are successfully dealt with. Furthermore, a finite-time adaptive neural controller is designed based on command filter, which not only guarantees the finite-time stability of the system, but also makes the tracking errors reach a predefined bound in a finite-time. Stability analysis proves that the proposed method is feasible, and simulation verifies its effectiveness.

Suggested Citation

  • Wu, Ziwen & Zhang, Tianping & Xia, Xiaonan & Hua, Yu, 2022. "Finite-time adaptive neural command filtered control for non-strict feedback uncertain multi-agent systems including prescribed performance and input nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    • Handle: RePEc:eee:apmaco:v:421:y:2022:i:c:s009630032200039x
    DOI: 10.1016/j.amc.2022.126953
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    References listed on IDEAS

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    1. Yan-Hui Jing & Guang-Hong Yang, 2017. "Adaptive quantised fault-tolerant tracking control of uncertain nonlinear systems with unknown control direction and the prescribed accuracy," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(13), pages 2826-2837, October.
    2. Li, Zheng & Wang, Fang & Zhu, Ruitai, 2021. "Finite-time adaptive neural control of nonlinear systems with unknown output hysteresis," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    3. Wenjie Si & Xunde Dong & Feifei Yang, 2017. "Adaptive neural prescribed performance control for a class of strict-feedback stochastic nonlinear systems under arbitrary switchings," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(11), pages 2300-2310, August.
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    Citations

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    Cited by:

    1. Wang, Boyu & Zhang, Yijun & Wei, Miao, 2023. "Fixed-time leader-following consensus of multi-agent systems with intermittent control," Applied Mathematics and Computation, Elsevier, vol. 438(C).
    2. Truong, Hoai Vu Anh & Phan, Van Du & Tran, Duc Thien & Ahn, Kyoung Kwan, 2024. "A novel observer-based neural-network finite-time output control for high-order uncertain nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    3. Zhang, Yanqi & Wang, Zhenlei & Wang, Xin, 2023. "Adaptive modified prescribed performance constraint control for uncertain nonlinear discrete-time systems," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    4. 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).
    5. Xiongfeng Deng & Xiyu Zhang, 2022. "Adaptive Fuzzy Tracking Control of Uncertain Nonlinear Multi-Agent Systems with Unknown Control Directions and a Dead-Zone Fault," Mathematics, MDPI, vol. 10(15), pages 1-19, July.

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