Leader Selection in Multi-Agent Networks with Switching Topologies via Submodular Optimization

TL;DR

This paper addresses the problem of selecting minimal leader sets in multi-agent networks with switching topologies, using submodular optimization to ensure convergence to desired states and providing an algorithm with provable optimality bounds.

Contribution

It introduces a submodular optimization framework for minimal leader selection in high-order linear multi-agent networks with switching topologies and dwell times.

Findings

Proposes a sufficient condition for agent state convergence.

Develops a submodular optimization-based leader selection method.

Provides an algorithm with provable optimality bounds.

Abstract

In leader-follower multi-agent networks with switching topologies, choosing a subset of agents as leaders is a critical step to achieve desired performances. In this paper, we concentrate on the problem of selecting a minimum-size set of leaders that ensure the tracking of a reference signal in a highorder linear multi-agent network with a set of given topology dependent dwell time (TDDT). First, we derive a sufficient condition that guarantees the states of all agents converging to an expected state trajectory. Second, by exploiting submodular optimization method, we formulate the problem of identifying a minimal leader set which satisfies the proposed sufficient condition. Third, we present an algorithm with the provable optimality bound to solve the formulated problem. Finally, several numerical examples are provided to verify the effectiveness of the designed selection scheme.