Identification of Diffusively Coupled Linear Networks Through Structured Polynomial Models

• 成果类型: Article
• 署名作者: Kivits, E. M. M.; van den Hof, Paul M. J.
• 署名单位: Eindhoven University of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3191406
• 发表日期: 2023
• 页码: 3513-3528
• 关键词: Couplings; Object recognition; Integrated circuit interconnections; Power system dynamics; Heuristic algorithms; TOPOLOGY; Springs; data-driven modeling; diffusive couplings; linear dynamic networks; Parameter Estimation; physical networks; System identification

摘要

Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. These diffusive couplings imply that the cause-effect relationships in the interconnections are symmetric, and therefore, physical dynamic networks can be represented by undirected graphs. This article shows how prediction error identification methods developed for linear time-invariant systems in polynomial form can be configured to consistently identify the parameters and the interconnection structure of diffusively coupled networks. Furthermore, a multistep least squares convex optimization algorithm is developed to solve the nonconvex optimization problem that results from the identification method.