Clustering as a means of leader selection in consensus networks

TL;DR

This paper proposes a novel leader selection method for consensus networks using graph clustering algorithms, achieving higher accuracy than existing fast algorithms.

Contribution

It introduces a clustering-based leader selection approach for linear consensus networks, improving accuracy while maintaining computational efficiency.

Findings

Clustering-based leader selection outperforms existing algorithms in accuracy.

The method is computationally efficient and suitable for large networks.

It provides a natural link between control theory and network clustering techniques.

Abstract

In the leader-follower approach, one or more agents are selected as leaders who do not change their states or have autonomous dynamics and can influence other agents, while the other agents, called followers, perform a simple protocol based on the states of their neighbors. This approach provides a natural link between control theory and networked agents with their input data. Despite the fact that the leader-follower approach is widely used, the fundamental question still remains: how to choose leaders from a set of agent. This question is called the problem of choosing leaders. There is still no selection algorithm that is both optimal under a natural criterion and fast. In this paper, for agents that obey a linear consensus protocol, we propose to choose leaders using graph nodes' clustering algorithms and show that this method is the most accurate among the fast existing algorithms of choosing leaders.