Multi-Agent Consensus on Noisy Networks with Antagonistic Interactions

Abstract

This paper deals with a consensus problem for multiagent networks with noise-corrupted measurements. The networks are assumed to have antagonistic interactions that represent a kind of competition between agents. The objective of this paper is to find a condition under which global consensus on the states of the agents is achieved. In particular, a rigorous stopping rule is derived for the consensus algorithm. It gives a certain number of updates so that all of the states get close to the average value of them within a specified tolerance and with a specified probability.