Controllability Scores for Selecting Control Nodes of Large-Scale Network Systems

• 成果类型: Article
• 署名作者: Sato, Kazuhiro; Terasaki, Shun
• 署名单位: University of Tokyo
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3355806
• 发表日期: 2024
• 页码: 4673-4680
• 关键词: Controllability; Network systems; linear programming; gradient methods; Ellipsoids; Eigenvalues and eigenfunctions; Convex functions; centrality; CONTROLLABILITY; Convex Optimization; Large-scale system; projected gradient method

摘要

To appropriately select control nodes in a large-scale network system, we propose two control centralities called volumetric and average energy controllability scores. The scores are the unique solutions to convex optimization problems formulated using the controllability Gramian. The uniqueness is proven for stable cases and for unstable cases that include multiagent systems. We show that the scores can be efficiently calculated by using a proposed algorithm based on the projected gradient method on the standard simplex. Numerical experiments demonstrate that the proposed algorithm is more efficient than an existing interior point method, and the proposed scores can correctly capture the importance of each state node on controllability, outperforming existing control centralities.