Model Reduction of Linear Multi-Agent Systems by Clustering and Associated $\mathcal{H}_2$- and $\mathcal{H}_\infty$-Error Bounds

TL;DR

This paper presents a model reduction method for multi-agent systems on weighted graphs, providing error bounds based on clustering strategies and spectral properties, applicable to both equitable and arbitrary partitions.

Contribution

It introduces a clustering-based model reduction technique with explicit $ ext{H}_2$ and $ ext{H}_ ext{infty}$ error bounds for multi-agent systems, extending to non-equitable partitions.

Findings

Derived upper bounds depend on Laplacian eigenvalues and cluster structure.

Reduced networks preserve key structural properties of original networks.

Error bounds are applicable to both equitable and arbitrary clustering schemes.

Abstract

In this paper, we study a model reduction technique for leader-follower networked multi-agent systems defined on weighted, undirected graphs with arbitrary linear multivariable agent dynamics. In the network graph of this network, nodes represent the agents and edges represent communication links between the agents. Only the leaders in the network receive an external input, the followers only exchange information with their neighbors. The reduced network is obtained by partitioning the set of nodes into disjoint sets, called clusters, and associating with each cluster a single, new, node in a reduced network graph. The resulting reduced network has a weighted, symmetric, directed network graph, and inherits some of the structure of the original network. We establish a priori upper bounds on the $\mathcal{H}_2$ and $\mathcal{H}_\infty$ model reduction error for the special case that the graph partition is almost equitable. These upper bounds depend on the Laplacian eigenvalues of the original and reduced network, an auxiliary system associated with the agent dynamics, and the number of nodes that belong to the same clusters as the leaders in the network. Finally, we consider the problem of obtaining a priori upper bounds if we cluster using arbitrary, possibly non almost equitable, partitions.