Continuous‐time multi‐agent averaging with relative‐state‐dependent measurement noises: matrix intensity functions

Abstract

In this study, the distributed averaging of high-dimensional first-order agents is investigated with relative-statedependent measurement noises. Each agent can measure or receive its neighbours' state information with random noises, whose intensity is a non-linear matrix function of agents' relative states. By the tools of stochastic differential equations and algebraic graph theory, the authors give sufficient conditions to ensure mean square and almost sure average consensus and the convergence rate and the steady-state error for average consensus are quantified. Especially, if the noise intensity function depends linearly on the relative distance of agents' states, then a sufficient condition is given in terms of the control gain, the noise intensity coefficient constant, the number of agents and the dimension of agents' dynamics.

Stability analysis for a class of switched systems under perturbations with applications to consensus

“…Recently, the consensus problem for multi-agent systems (MASs) with noises under switching topologies, which can be regraded as a special class of switched systems with perturbations, has received much attention [14][15][16][17][18]. For example, reference [17] gives a sufficient condition to guarantee the m.s.…”

Synchronization of General Linear Multi‐Agent Systems With Measurement Noises

“…Many researchers have considered the distributed control of multi‐agent networks with measurement noises, such as and references therein. In and , they studied the stochastic consensus problem of networked agents, with or without switching topology, in the discrete‐time setting.…”

“…It proposes a consensus protocol that takes into account both the measurement noises and general time‐varying communication time‐delays. References and considered the distributed consensus of high‐dimensional first‐order agents with relative‐state‐dependent measurement noises. They develop several small consensus gain theorems to give sufficient conditions to ensure mean square and almost sure consensus and quantify the convergence rate and the steady‐state error.…”

Robust consensus for linear multi‐agent systems with noises

“…Liu et al [15] considered the consensus problem for first‐order integral MASs with both communication noises and delays, and the stochastic approximation‐type consensus protocol was still valid in this situation. In [16], the distributed average consensus of high‐dimensional first‐order integral agents with relative‐state‐dependent communication noises was investigated. Cheng et al [17] presented an average consensus protocol for continuous‐time double‐integrator MASs with communication noises.…”