Exploiting the Agent's Memory in Asymptotic and Finite-Time Consensus Over Multi-Agent Networks
Gianni Pasolini, Davide Dardari, Michel Kieffer
2020IEEE Transactions on Signal and Information Processing over Networks21 citationsDOIOpen Access PDF
Abstract
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 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.
Topics & Concepts
Robustness (evolution)A priori and a posterioriComputer scienceNetwork topologyConvergence (economics)ConsensusMulti-agent systemTransmission (telecommunications)Topology (electrical circuits)Overhead (engineering)Distributed computingAlgorithmMathematicsComputer networkArtificial intelligenceTelecommunicationsCombinatoricsEpistemologyEconomicsChemistryPhilosophyGeneOperating systemBiochemistryEconomic growthDistributed Control Multi-Agent SystemsEnergy Efficient Wireless Sensor NetworksDistributed Sensor Networks and Detection Algorithms