Event-Triggered Average Consensus on Matrix-Weighted Networks

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.