A Scalable Approach for Analysing Multi-Agent Systems with Heterogeneous Stochastic Packet Loss

TL;DR

This paper presents a scalable, robust control-based method for analyzing large multi-agent systems with heterogeneous and temporally correlated stochastic packet loss, enabling efficient modeling of complex networked control systems.

Contribution

It extends previous models by incorporating heterogeneous transmission probabilities and temporal correlation, formulated through linear matrix inequalities for scalability.

Findings

Applicable to systems with up to 10,000 agents

Linear growth of complexity with number of agents

Demonstrated robustness and scalability through simulations

Abstract

An important aspect in jointly analysing networked control systems and their communication is to model the networking in a sufficiently rich but at the same time mathematically tractable way. As such, this paper improves on a recently proposed scalable approach for analysing multi-agent systems with stochastic packet loss by allowing for heterogeneous transmission probabilities and temporal correlation in the communication model. The key idea is to consider the transmission probabilities as uncertain, which facilitates the use of tools from robust control. Due to being formulated in terms of linear matrix inequalities that grow linearly with the number of agents, the result is applicable to very large multi-agent systems, which is demonstrated by numerical simulations with up to 10000 agents.