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Fully distributed neural control of periodically time-varying parameterized stochastic nonlinear multi-agent systems with hybrid-order dynamics

Author

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  • Chen, Jiaxi
  • Yu, Zehua
  • Liu, Sanyang
  • Li, Junmin
  • Xie, Jin

Abstract

In this article, a new fully distributed neural control is presented for periodically time-varying parameterized stochastic nonlinear multi-agent systems with hybrid-order dynamics. All follower systems are not required to be nonlinear dynamics of the same-order. The unknown periodically time-varying nonlinear function is described by using neural networks and Fourier series expansion in the design. It is proved that the presented distributed adaptive method can ensure that all closed-loop signals are bounded in the sense of probability. Furthermore, the state variable of the closed-loop system can be proved to converge to an arbitrary small neighborhood of the zero in the mean square sense. Simulation results are presented to demonstrate the effectiveness of the proposed distributed algorithm.

Suggested Citation

  • Chen, Jiaxi & Yu, Zehua & Liu, Sanyang & Li, Junmin & Xie, Jin, 2022. "Fully distributed neural control of periodically time-varying parameterized stochastic nonlinear multi-agent systems with hybrid-order dynamics," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    • Handle: RePEc:eee:apmaco:v:426:y:2022:i:c:s0096300322002016
    DOI: 10.1016/j.amc.2022.127117
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    References listed on IDEAS

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    1. Fan, Ming-Can & Wu, Yue, 2018. "Global leader-following consensus of nonlinear multi-agent systems with unknown control directions and unknown external disturbances," Applied Mathematics and Computation, Elsevier, vol. 331(C), pages 274-286.
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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).

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