Exploiting the Agent's Memory in Asymptotic and Finite-Time Consensus Over Multi-Agent Networks

This article proposes two average consensus algorithms exploiting the memory of agents. The performance of the proposed as well as of several state-of-the-art consensus algorithms is evaluated considering different communication ranges, and evaluating the impact of transmission errors. To compare asymptotic and finite-time average consensus schemes, the $\varepsilon$-convergence time is adopted for a fair comparison. A discussion about memory requirements, transmission overhead, a priori information on network topology, and robustness to errors is provided.