Dynamic Average Consensus with Distributed Event-triggered Communication

This paper analyzes distributed algorithmic solutions to dynamic average consensus implemented in continuous time and relying on communication at discrete instants of time. Our starting point is a distributed coordination strategy that, under continuous-time communication, achieves practical asymptotic tracking of the dynamic average of the time-varying agents' inputs. We propose two different distributed event-triggered communication laws, depending on whether the interaction topology is described by a strongly connected and weight-balanced digraph or an undirected connected graph. In both cases, we establish positive lower bounds on the inter-event times of each agent and characterize their dependence of the algorithm design parameters. We build on this result to rule out the presence of Zeno behavior and characterize the asymptotic correctness of the resulting implementations. Simulations illustrate the results.