Command-Filter-Based Fixed-Time Bipartite Containment Control for a Class of Stochastic Multiagent Systems

This article studies the command-filter-based fixed-time bipartite containment control problem for a class of nonlinear stochastic multiagent systems (MASs). The considered stochastic MASs in nonstrict feedback form is subject to unknown nonlinear functions and stochastic disturbances, which can be solved by exploiting the universal approximation property of radial basis function neural networks. In addition, the event-triggered mechanism is used to improve the utilization of communication resources while avoiding Zeno behavior. The control protocol based on the command-filtered backstepping technique is proposed to ensure that the followers can converge to the convex hull formed by the leaders. Moreover, the closed-loop stability of stochastic MASs is proved to be semiglobal practical fixed-time stability. Finally, a numerical example simulation and an actual system simulation about a group of five single-link manipulator systems are presented to verify the effectiveness of the proposed method.