Partitioning and Observability in Linear Systems via Submodular Optimization

• 成果类型: Article
• 署名作者: Kazma, Mohamad H.; Taha, Ahmad F.
• 署名单位: Vanderbilt University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3676329
• 发表日期: 2026
• 关键词: SENSOR SELECTION; CONTROLLABILITY; networks; algorithms

摘要

Network partitioning has gained recent attention as a pathway to enable decentralized operation and control in large-scale systems. This article addresses the interplay between partitioning, observability, and sensor placement (SP) in dynamic networks. The problem, being computationally intractable at scale, is a largely unexplored, open problem in the literature. To that end, this article's objective is designing scalable partitioning of linear systems while maximizing observability metrics of the subsystems. We show that the partitioning problem can be posed as a submodular maximization problem-and the SP problem can subsequently be solved over the partitioned network. Consequently, theoretical bounds are derived to compare observability metrics of the original network with those of the resulting partitions, highlighting the impact of partitioning on system observability. Case studies on networks of varying sizes corroborate the derived theoretical bounds.