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

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

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  • NSTL
  • Elsevier
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.(c) 2022 Elsevier Inc. All rights reserved.

Adaptive neural controlPeriodic disturbancesFourier series expansionHybrid-order dynamicsStochastic multi-agent systemsDOUBLE-INTEGRATOR DYNAMICSCONTAINMENT CONTROLTRACKING CONTROLLEARNING CONTROLCONSENSUSSYNCHRONIZATIONALGORITHMSNETWORKS

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

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Xidian Univ

2022

10.1016/j.amc.2022.127117

Applied mathematics and computation

Applied mathematics and computation

EISCI
ISSN：0096-3003
年,卷(期)：2022.426
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