Scalable {\delta}-Level Coherent State Synchronization of Multi-Agent Systems in the Presence of Bounded Disturbances

TL;DR

This paper introduces a scalable, model-based framework for achieving $\, ext{ extdelta}$-level coherent state synchronization in multi-agent systems with bounded disturbances, independent of network size or topology.

Contribution

It presents a scale-free, disturbance-agnostic approach for coherent synchronization that relies only on agent models, not on communication graph or network size.

Findings

Achieves $\, extdelta$-level coherence in multi-agent systems.

Framework is scalable and independent of network topology.

Effective in the presence of bounded disturbances.

Abstract

In this paper, we study scalable $\delta-$level coherent state synchronization for multi-agent systems (MAS) where the agents are subject to bounded disturbances/noises. We propose a scale-free framework designed solely based on the knowledge of agent models and agnostic to the communication graph and the size of the network. We define the level of coherency for each agent as the norm of the weighted sum of the disagreement dynamics with its neighbors. The objective is to restrict the network's coherency level to $\delta$ without a-priori information about the disturbance.