Gradient-Based Distributed Controller Design Over Directed Networks

TL;DR

This paper introduces a novel gradient-based distributed control design for multi-agent systems over directed networks, improving convergence and performance, and demonstrating its effectiveness through numerical examples.

Contribution

It extends the gradient-flow method to directed networks and applies it to dynamic matching problems in time-varying multi-agent systems.

Findings

Enhanced convergence properties over directed networks

Successful application to dynamic matching in time-varying networks

Numerical validation of the proposed control method

Abstract

In this study, we propose a design methodology of distributed controllers for multi-agent systems on a class of directed interaction networks by extending the gradient-flow method. Although the gradient-flow method is a common design tool for distributed controllers, it is inapplicable to directed networks. First, we demonstrate how to construct a distributed controller for systems over a class of time-invariant directed graphs. Subsequently, we establish better convergence properties and performance enhancement than the conventional gradient-flow method. To illustrate its application in time-varying networks, we address the dynamic matching problem of two distinct groups of agents with different sensing ranges. This problem is a novel coordination task that involves pairing agents from two distinct groups to achieve a convergence of the paired agents' states to the same value. Accordingly, we apply the proposed method to this problem and provide sufficient conditions for successful matching. Lastly, numerical examples for systems on both time-invariant and time-varying networks demonstrate the effectiveness of the proposed method.