Scale-Free delta-Level Coherent Output Synchronization of Multi-Agent Systems with Adaptive Protocols and Bounded Disturbances
Anton A. Stoorvogel, Ali Saberi, Donya Nojavanzadeh
TL;DR
The paper addresses the challenge of achieving scale-free $\delta$-level coherent output synchronization in MAS under bounded disturbances without knowledge of network topology or size. It proposes two adaptive, distributed protocols (noncollaborative and collaborative) designed solely from agent models, leveraging a Riccati-based decomposition and Lyapunov analysis to guarantee $\| abla_i\|$ coherence below a prescribed $\delta$ for any $N$ and graph. The key contributions include solvability results under minimal assumptions, a collaborative variant that relaxes agent-model requirements (with a precompensator to enforce uniform rank if needed), and extensive numerical demonstrations across diverse graph types, sizes, and disturbance patterns. The work provides scalable, topology-agnostic methods for robust synchronization in large-scale MAS with practical implications for robotics, sensor networks, and autonomous systems.
Abstract
In this paper, we investigate scale-free delta-level coherent output synchronization for multi-agent systems (MAS) operating under bounded disturbances or noises. We introduce an adaptive scale-free framework designed solely based on the knowledge of agent models and completely agnostic to both the communication topology 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. We define each agents coherency level as the norm of a weighted sum of its disagreement dynamics relative to its neighbors. The goal is to ensure that the networks coherency level remains below a prescribed threshold delta, without requiring any a priori knowledge of the disturbance.