Consensus-Based Stability Analysis of Multi-Agent Networks

TL;DR

This paper introduces a distributed stability analysis method for large-scale multi-agent systems, enabling controllers to be designed using only local information, which reduces the need for centralized computation.

Contribution

It develops a consensus-based distributed stability analysis approach utilizing Vidyasagar's Network Dissipativity Theorem and ADMM, suitable for sparse communication networks.

Findings

Algorithms converge in numerical examples with UAV swarms.

The method effectively analyzes stability using local agent information.

Reduces reliance on centralized data exchange.

Abstract

The emergence of large-scale multi-agent systems has led to controller synthesis methods for sparse communication between agents. However, most sparse controller synthesis algorithms remain centralized, requiring information exchange and high computational costs. This underscores the need for distributed algorithms that design controllers using only local dynamics information from each agent. This paper presents a consensus-based distributed stability analysis. The proposed stability analysis algorithms leverage Vidyasagar's Network Dissipativity Theorem and the alternating direction methods of multipliers to perform general stability analysis. Numerical examples involving a 2D swarm of unmanned aerial vehicles demonstrate the convergence of the proposed algorithms.