An Averaging Consensus Algorithm and Its Stopping Rule over Noisy Undirected Networks of MIMO Linear Symmetric Agents

Abstract

A stochastic averaging consensus algorithm is considered for a multi-agent system over a noisy undirected network with multi-input/multi-output (MIMO) linear symmetric agents. The convergence of the algorithm is investigated, which gives an explicit relation between the number of iterations and the closeness of the agreement, i.e., a stopping rule. The result is illustrated through a numerical example.