Kernel-Based Models for System Analysis

• 成果类型: Article
• 署名作者: van Waarde, Henk J.; Sepulchre, Rodolphe
• 署名单位: University of Groningen; University of Cambridge
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3218944
• 发表日期: 2023
• 页码: 5317-5332
• 关键词: Identification for control; Machine Learning; Modeling; Nonlinear systems; System identification

摘要

This article introduces a computational framework to identify nonlinear input-output operators that fit a set of system trajectories while satisfying incremental integral quadratic constraints. The data fitting algorithm is thus regularized by suitable input-output properties required for system analysis and control design. This biased identification problem is shown to admit the tractable solution of a regularized least squares problem when formulated in a suitable reproducing kernel Hilbert space. The kernel-based framework is a departure from the prevailing state-space framework. It is motivated by fundamental limitations of nonlinear state-space models at combining the fitting requirements of data-based modeling with the input-output requirements of system analysis and physical modeling.