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

TL;DR

This paper develops distributed event-triggered algorithms for dynamic average consensus in networked systems, reducing communication frequency while maintaining accurate tracking of time-varying inputs.

Contribution

It introduces novel event-triggered communication laws for dynamic average consensus, ensuring positive inter-event times and preventing Zeno behavior in directed and undirected networks.

Findings

Positive lower bounds on inter-event times established.

Algorithms achieve practical asymptotic tracking.

Simulations validate theoretical results.

Abstract

This paper presents distributed algorithmic solutions that employ opportunistic inter-agent communication to achieve dynamic average consensus. In our solutions each agent is endowed with a local criterion that enables it to determine whether to broadcast its state to its neighbors. Our starting point is a continuous-time distributed coordination strategy that, under continuous-time communication, achieves practical asymptotic tracking of the dynamic average of the time-varying agents' reference inputs. Then, for this algorithm, depending on the directed or undirected nature of the time-varying interactions and under suitable connectivity conditions, we propose two different distributed event-triggered communication laws 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 on 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.