Minimum Clustering of Matrices Based on Phase Alignment

TL;DR

This paper presents a phase-alignment-based clustering framework to reduce controller diversity in multi-agent systems, balancing synchronization performance and implementation costs through hierarchical optimization.

Contribution

It introduces a novel clustering method based on phase alignment of complex matrices, combining exact and stochastic optimization for scalable controller minimization.

Findings

Effective controller reduction in a 50-agent network

Hierarchical optimization improves scalability

Bridges phase analysis with control synthesis

Abstract

Coordinating multi-agent systems requires balancing synchronization performance and controller implementation costs. To this end, we classify agents by their intrinsic properties, enabling each group to be controlled by a uniform controller and thus reducing the number of unique controller types required. Existing centralized control methods, despite their capability to achieve high synchronization performance with fewer types of controllers, suffer from critical drawbacks such as limited scalability and vulnerability to single points of failure. On the other hand, distributed control strategies, where controllers are typically agent-dependent, result in the type of required controllers increasing proportionally with the size of the system. This paper introduces a novel phase-alignment-based framework to minimize the type of controllers by strategically clustering agents with aligned synchronization behaviors. Leveraging the intrinsic phase properties of complex matrices, we formulate a constrained clustering problem and propose a hierarchical optimization method combining recursive exact searches for small-scale systems and scalable stochastic approximations for large-scale networks. This work bridges theoretical phase analysis with practical control synthesis, offering a cost-effective solution for large-scale multi-agent systems. The theoretical results applied for the analysis of a 50-agent network illustrate the effectiveness of the proposed algorithms.