This paper 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 ϵ-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.
Exploiting the Agent's Memory in Asymptotic and Finite-time Consensus over Multi-agent Networks / Pasolini G.; Dardari D.; Kieffer M.. - In: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS. - ISSN 2373-776X. - ELETTRONICO. - 6:(2020), pp. 479-490. [10.1109/TSIPN.2020.3002613]
Exploiting the Agent's Memory in Asymptotic and Finite-time Consensus over Multi-agent Networks
Pasolini G.
;Dardari D.;
2020
Abstract
This paper 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 ϵ-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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.