Robust dynamic average consensus with prescribed transient and steady state performance

Abstract

In this paper, we consider the dynamic average consensus problem for a group of multiple agents that cooperate to estimate the average of locally available time-varying reference signals, under an undirected communication topology. More specifically, we develop a novel robust distributed estimation algorithm, capable of achieving practically zero average tracking error even for fast time-varying reference signals. The proposed scheme is purely distributed, since the estimation algorithm is based solely on local computations and local communication among neighboring agents, and guarantees prescribed performance in the sense that any convergence rate and steady state deviation among the agents’ estimates may be achieved via the appropriate selection of certain design parameters. Moreover, the consensus and average tracking performance are fully decoupled, thus allowing easy tuning of the gains. Additionally, intermittent implementation is proposed via appropriately selected event- and self-triggering mechanisms. Finally, the approach is clarified and verified through various simulation paradigms.