Distributed Robust Dynamic Average Consensus with Dynamic Event-Triggered Communication

TL;DR

This paper develops a distributed, robust dynamic average consensus algorithm that uses event-triggered communication to reduce communication load while maintaining stability and robustness against network disruptions.

Contribution

It introduces a novel dynamic event-triggered scheme for robust consensus, reducing communication without sacrificing stability in dynamic networks.

Findings

Algorithm is asymptotically stable

Reduces communication load

Free of Zeno behavior

Abstract

This paper presents the formulation and analysis of a fully distributed dynamic event-triggered communication based robust dynamic average consensus algorithm. Dynamic average consensus problem involves a networked set of agents estimating the time-varying average of dynamic reference signals locally available to individual agents. We propose an asymptotically stable solution to the dynamic average consensus problem that is robust to network disruptions. Since this robust algorithm requires continuous communication among agents, we introduce a novel dynamic event-triggered communication scheme to reduce the overall inter-agent communications. It is shown that the event-triggered algorithm is asymptotically stable and free of Zeno behavior. Numerical simulations are provided to illustrate the effectiveness of the proposed algorithm.