Cluster consensus in discrete-time networks of multi-agents with inter-cluster nonidentical inputs

TL;DR

This paper investigates cluster consensus in discrete-time multi-agent systems with time-varying topologies and inter-cluster nonidentical inputs, extending existing theories to ensure intra-cluster synchronization and inter-cluster separation.

Contribution

It extends consensus theories to include cluster consensus with nonidentical inputs and time-varying graphs, providing conditions for intra-cluster synchronization and inter-cluster separation.

Findings

Cluster spanning trees ensure intra-cluster synchronization.

Time-varying graphs with union spanning trees achieve synchronization.

Inter-cluster inputs guarantee inter-cluster separation.

Abstract

In this paper, cluster consensus of multi-agent systems is studied via inter-cluster nonidentical inputs. Here, we consider general graph topologies, which might be time-varying. The cluster consensus is defined by two aspects: the intra-cluster synchronization, that the state differences between each pair of agents in the same cluster converge to zero, and inter-cluster separation, that the states of the agents in different clusters are separated. For intra-cluster synchronization, the concepts and theories of consensus including the spanning trees, scramblingness, infinite stochastic matrix product and Hajnal inequality, are extended. With them, it is proved that if the graph has cluster spanning trees and all vertices self-linked, then static linear system can realize intra-cluster synchronization. For the time-varying coupling cases, it is proved that if there exists T>0 such that the union graph across any T-length time interval has cluster spanning trees and all graphs has all vertices self-linked, then the time-varying linear system can also realize intra-cluster synchronization. Under the assumption of common inter-cluster influence, a sort of inter-cluster nonidentical inputs are utilized to realize inter-cluster separation, that each agent in the same cluster receives the same inputs and agents in different clusters have different inputs. In addition, the boundedness of the infinite sum of the inputs can guarantee the boundedness of the trajectory. As an application, we employ a modified non-Bayesian social learning model to illustrate the effectiveness of our results.