Distributed event-triggered communication for dynamic average consensus in networked systems

This paper presents distributed algorithmic solutions that employ opportunistic inter-agent communication to achieve dynamic average consensus. Our solutions endow individual agents with autonomous criteria that can be checked with the information available to them in order to determine whether to broadcast their state to their neighbors. 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 the directed or undirected nature of the time-varying interactions and under suitable connectivity conditions, that prescribe agent communications at discrete time instants in an opportunistic fashion. 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. This analysis allows us to rule out the presence of Zeno behavior and characterize the asymptotic correctness of the resulting implementations. Several simulations illustrate the results.