Cooperative Estimation for Synchronization of Heterogeneous Multi-Agent Systems Using Relative Information

TL;DR

This paper introduces a scalable distributed estimation method for heterogeneous multi-agent systems that uses only relative measurements, ensuring robustness and enabling output synchronization.

Contribution

It proposes a novel estimation algorithm that improves scalability by limiting agents to local state estimation and guarantees robustness against disturbances.

Findings

Algorithm achieves robust local state estimation.

Ensures scalability for large heterogeneous systems.

Integrates with output synchronization methods.

Abstract

In this paper, we present a distributed estimation setup where local agents estimate their states from relative measurements received from their neighbours. In the case of heterogeneous multi-agent systems, where only relative measurements are available, this is of high relevance. The objective is to improve the scalability of the existing distributed estimation algorithms by restricting the agents to estimating only their local states and those of immediate neighbours. The presented estimation algorithm also guarantees robust performance against model and measurement disturbances. It is shown that it can be integrated into output synchronization algorithms.