Adaptive neural containment seeking of stochastic nonlinear strict-feedback multi-agent systems

Abstract

The paper addresses a distributed containment issue for a stochastic nonlinear strict-feedback multi-agent system. In combination with the graph theory and neural networks technique, an adaptive neural containment protocol is developed within the traditional backstepping framework. Such a containment protocol allows one to extend some existing results to the case that multiple followers are confined to stochastic nonlinear dynamics. Furthermore, with the help of the quartic Lyapunov function, it is proved that all the signals in the resulting closed-loop system remain bounded in probability and the containment error converges to a small neighborhood of the origin in the sense of mean square by a suitable choice of parameters. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed protocol.