Event-Triggered Average Consensus on Matrix-Weighted Networks
Kaien Liu, Zhijian Ji
Abstract
Consensus of multi-agent systems is investigated. To characterize interdependencies among multi-dimensional states of neighboring agents, matrix-weighted networks are utilized. Event-triggered mechanism is proposed. By introducing positive functions into the thresholds, Zeno behavior is excluded. Moreover, theoretical analysis shows that these functions can affect the consensus convergence speed. Necessary and sufficient conditions for achieving average consensus are given. Both algebraic and topological conditions are discussed. Finally, theoretical results are verified by numerical simulation.
Topics & Concepts
Convergence (economics)Zeno's paradoxesComputer scienceMatrix (chemical analysis)Multi-agent systemConsensusAlgebraic numberEvent (particle physics)Topology (electrical circuits)MathematicsMathematical optimizationArtificial intelligencePhysicsCombinatoricsMathematical analysisGeometryEconomicsQuantum mechanicsComposite materialMaterials scienceEconomic growthDistributed Control Multi-Agent SystemsNeural Networks Stability and SynchronizationOpinion Dynamics and Social Influence