A Kernel-Based Approach to Physics-Informed Nonlinear System Identification

• 成果类型: Article
• 署名作者: Donati, Cesare; Mammarella, Martina; Calafiore, Giuseppe C.; Dabbene, Fabrizio; Lagoa, Constantino; Novara, Carlo
• 署名单位: Polytechnic University of Turin; Consiglio Nazionale delle Ricerche (CNR); Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3663111
• 发表日期: 2026
• 关键词: model

摘要

This article presents a kernel-based framework for physics-informed nonlinear system identification that extends kernel-based techniques, accounting for unmodeled dynamics, to seamlessly embed partially known physics-based models, improving parameter estimation and overall model accuracy. The two models' components are identified from data simultaneously, thereby minimizing a suitable cost that balances the relative importance of the physical and the black-box parts of the model. In addition, nonlinear state smoothing is employed to address scenarios involving state-space models with not-fully measurable states. Numerical simulations on an experimental benchmark system demonstrate the effectiveness of the proposed approach, achieving up to 51% reduction in simulation root mean square error compared to physics-only models and 31% performance improvement over state-of-the-art identification techniques.