Distributed Consensus Control of Nonlinear Multiagent Systems With Output Noises via a Switching Classification Approach

Abstract

This article explores the distributed leader-following consensus control problem of nonlinear multiagent systems subject to stochastic output sensing noises under a fixed directed topology. Different from existing research, we pay attention to the effect of multiplicative stochastic noises on the sensors, and the noise intensity can be described as an unknown time-varying function with an arbitrarily large bound. In this condition, we put forward the noise intensity classification-based distributed output feedback consensus approach. First, we divide the noise intensity into small-noise intensity and large-noise intensity by designing a demarcation constant, and we introduce the switching property to distinguish them. Then, for the follower, we design the distributed controller based on the established compensator utilizing relevant outputs, and we produce sufficient conditions for the controller to be implemented. Subsequently, a unified Lyapunov function is constructed to prove that all agents can realize the full-state exponential consensus in mean square under the action of the controller. Finally, the simulation example is provided to demonstrate the efficiency of our approach.