Robust Dynamic Average Consensus with Prescribed Performance

We consider the dynamic average consensus problem in which multiple agents cooperate in order to track the average of locally available time-varying reference signals. Within this framework, each agent is only capable of local computations and local communication with its neighbors. Hence, we propose a distributed estimation procedure that guarantees bounded tracking error, with practical asymptotic convergence at the steady state even for fast time-varying reference signals. The transient needed for the agents to reach consensus and the maximum deviation among their estimates can be set arbitrarily small a priori, via the appropriate selection of certain design parameters. Moreover, the consensus and the tracking performance are regulated independently. Finally, we validate the proposed scheme through various simulated paradigms.