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

Highlights：

• The distributed control protocol designed in this article has a wide range of applications, because the modeling system of follower not only contains the dynamic of periodic time-varying nonlinear parameterization, but also considers the influence of random factors.

• A new fully distributed control based on local neighbor information is designed by adding a time-varying adaptive gain and constructing a new Lyapunov functional.

摘要

•This article first attempts to solve the distributed adaptive consensus problem of a class of stochastic mixed first- and second-order periodic time-varying nonlinear parameterized MASs by using the NNs, FSE and adaptive design method.•The distributed control protocol designed in this article has a wide range of applications, because the modeling system of follower not only contains the dynamic of periodic time-varying nonlinear parameterization, but also considers the influence of random factors.•A new fully distributed control based on local neighbor information is designed by adding a time-varying adaptive gain and constructing a new Lyapunov functional.

论文关键词：Adaptive neural control,Periodic disturbances,Fourier series expansion,Hybrid-order dynamics,Stochastic multi-agent systems

论文评审过程：Received 17 May 2021, Revised 21 December 2021, Accepted 21 March 2022, Available online 3 April 2022, Version of Record 3 April 2022.