Reaching a stochastic consensus in the noisy networks of linear MIMO agents: Dynamic output-feedback and convergence rate

Abstract

This paper addresses the leader-following consensus problem of linear multi-agent systems (MASs) with communication noise. Each agent's dynamical behavior is described by a linear multi-input and multi-output (MIMO) system, and the agent's full state is assumed to be unavailable. To deal with this challenge, a state observer is constructed to estimate the agent's full state. A dynamic output-feedback based protocol that is based on the estimated state is proposed. To mitigate the effect of communication noise, noise-attenuation gains are also introduced into the proposed protocol. In this study, each agent is allowed to have its own noise-attenuation gain. It is shown that the proposed protocol can solve the mean square leader-following consensus problem of a linear MIMO MAS. Moreover, if all noise-attenuation gains are of (t  ), where ∈(0,1), the convergence rate of the MAS can be quantitatively analyzed. It turns out that all followers' states converge to the leader's state in the mean square sense at a rate of O(t  ). multi-agent system, mean square consensus, communication noise, noise-attenuation gain, convergence rate Citation:Wang Y P, Cheng L, Yang C G, et al. Reaching a stochastic consensus in the noisy networks of linear MIMO agents: Dynamic output-feedback and convergence rate.

Distributed adaptive neural network control for a class of heterogeneous nonlinear multi-agent systems subject to actuation failures

“…Besides the actuator failures considered in this paper, the communication network is usually affected by unavoidable noise, which will also cause the failure of the proposed consensus protocol. Some researchers have devoted themselves to mitigating the effect of communication\measurement noise (Li & Xie, 2012;Li & Zhang, 2010;Wang, Cheng, Ren, Hou, & Tan, 2015;Wang, Cheng, Yang, Hou, & Tan, 2016;Wang & Liu, 2009). Recently, Noise-attention gains are introduced in the novel consensus protocols to solve the consensus problem for general linear multi-agent systems with communication noise (Wang, Cheng, Ren, Hou, & Tan, 2015;Wang, Cheng, Yang, Hou, & Tan, 2016).…”