Subspace Consensus of Matrix-Weighted Networks

Yuhao Chen, Lulu Pan, Xiaohui Gong, Peng Wang, Haibin Shao

This paper investigates the subspace consensus problem of matrix-weighted multi-agent networks, where each agent possesses a vector-valued state in $\mathbb{R}^{d}$ and interactions between neighboring agents are characterized by matrix-valued edge weights. Besides all dimensions of the agent states achieve full-state consensus, many practical applications appeal that agents are required to agree only on certain dimensions while maintaining desired relative configurations in the remaining ones. To address this gap, we introduce the concept of subspace consensus. A matrix-weighted network is said to achieve subspace consensus on a subspace $\mathbb{V}\subseteq\mathbb{R}^{d}$ if the projection of the agents' state differences onto $\mathbb{V}$ asymptotically converges to zero. This definition renders the traditional consensus as a special case when $\mathbb{V}=\mathbb{R}^{d}$. From an algebraic perspective, we derive necessary and sufficient conditions for subspace consensus by analyzing the interplay between the null spaces of edge weights. From a topological perspective, we present sufficient conditions characterized by $\mathbb{V}$-connectivity and the existence of a $\mathbb{V}$-spanning tree, as well as necessary conditions based on graph cuts. Furthermore, we provide refined necessary and sufficient conditions specifically for tree networks. This work uncovers a fundamental capability inherent to matrix-weighted networks and establishes a systematic framework for analyzing agreement behaviors on prescribed subspaces.