A Linear Parameter-Varying Approach to Data Predictive Control

• 成果类型: Article
• 署名作者: Verhoek, Chris; Berberich, Julian; Haesaert, Sofie; Toth, Roland; Abbas, Hossam S.
• 署名单位: Eindhoven University of Technology; University of Stuttgart; University of Lubeck; HUN-REN; HUN-REN Institute for Computer Science & Control
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3626955
• 发表日期: 2026
• 关键词: data-driven control; TRACKING MPC; systems

摘要

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system.