Stable Linear System Identification With Prior Knowledge by Riemannian Sequential Quadratic Optimization

• 成果类型: Article
• 署名作者: Obara, Mitsuaki; Sato, Kazuhiro; Sakamoto, Hiroki; Okuno, Takayuki; Takeda, Akiko
• 署名单位: University of Tokyo; Seikei University; RIKEN
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3318195
• 发表日期: 2024
• 页码: 2060-2066
• 关键词: Riemannian nonlinear optimization (RNLO); riemannian sequential quadratic optimization (RSQO); stable linear system; System identification

摘要

We consider an identification method for a linear continuous time-invariant autonomous system from noisy state observations. In particular, we focus on the identification to satisfy the asymptotic stability of the system with some prior knowledge. To this end, we propose to model this identification problem as a Riemannian nonlinear optimization (RNLO) problem, where the stability is ensured through a certain Riemannian manifold and the prior knowledge is expressed as nonlinear constraints defined on this manifold. To solve this RNLO, we apply the Riemannian sequential quadratic optimization (RSQO) that was proposed by Obara, Okuno, and Takeda (2022) most recently. RSQO performs quite well with theoretical guarantee to find a point satisfying the Karush-Kuhn-Tucker conditions of RNLO. In this article, we demonstrate that the identification problem can be indeed solved by RSQO more effectively than competing algorithms.