Convergence Analysis of Stochastic Consensus over Noisy Networks of Linear Symmetric Agents

Abstract

A multi-agent system over noisy undirected networks with linear symmetric agents is considered. A stochastic consensus algorithm is established and analyzed under the noisy environment. An explicit relation between the number of iterations and the closeness of the agreement is also established. The results are illustrated through a numerical example.