A Kernel-Based Approach to Errors-in-Variables Identification of Stable Multivariable Linear Systems

• 成果类型: Article
• 署名作者: Cerone, Vito; Fadda, Edoardo; Regruto, Diego
• 署名单位: Polytechnic University of Turin; Polytechnic University of Turin
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3410835
• 发表日期: 2024
• 页码: 8481-8496
• 关键词: Kernel; Linear systems; MIMO communication; computational modeling; optimization; Hilbert space; System identification; Reproducing Kernel Hilbert Spaces; errors-in-variables

摘要

In this article, we present a kernel-based nonparametric approach to identifying stable multi-input multioutput (MIMO) linear systems in the presence of bounded noise affecting both the input and the output measurements. First, we formulate the considered problem in terms of robust optimization techniques. Then, we show that the formulated robust optimization problem can be solved using semidefinite optimization. Since the involved optimization problem is computationally demanding, we also provide a result that allows the user to compute a bound on the approximation error introduced by considering reduced complexity models. We present some simulation examples to show the effectiveness of the proposed approach. Finally, we apply the proposed identification method to the dataset experimentally collected on a linear electronic filter.