System Identification Based on Invariant Subspace

• 成果类型: Article
• 署名作者: Huang, Chao; Feng, Gang; Zhang, Hao; Wang, Zhuping
• 署名单位: Tongji University; City University of Hong Kong
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3069757
• 发表日期: 2022
• 页码: 1327-1341
• 关键词: Time-domain analysis; data models; Time-frequency analysis; Mathematical model; CONVERGENCE; STANDARDS; dynamical systems; computational methods; Linear systems; sampled data; System identification

摘要

This article proposes a novel system identification method based on the notion of invariant subspace. It is shown that when the system input and output asymptotically converge onto an invariant subspace, a new form of regression can be obtained. New identification algorithms are then developed based on the obtained regression. The proposed method has several distinctive advantages originating from both time-domain and frequency-domain approaches. They include: 1) linear continuous-time models can be identified from slowly sampled input/output data; 2) consistency of the model parameters can be established in an error-in-variables framework; 3) the global optimum can be found by solving two linear least-square problems; and 4) the identification algorithms can be implemented online with explicit convergence rates. The theoretic results are tested by numerical examples to show the effectiveness of the proposed method.