Data-Driven MPC With Stability Guarantees Using Extended Dynamic Mode Decomposition

• 成果类型: Article
• 署名作者: Bold, Lea; Gruene, Lars; Schaller, Manuel; Worthmann, Karl
• 署名单位: University of Bayreuth
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3431169
• 发表日期: 2025
• 页码: 534-541
• 关键词: Asymptotic stability; COSTS; Generators; Dictionaries; CONTROLLABILITY; Numerical stability; computational modeling; Cost controllability; data-driven; dynamic mode decomposition; model predictive control (MPC); STABILITY

摘要

For nonlinear (control) systems, extended dynamic mode decomposition (EDMD) is a popular method to obtain data-driven surrogate models. Its theoretical foundation is the Koopman framework, in which one propagates observable functions of the state to obtain a linear representation in an infinite-dimensional space. In this article, we prove practical asymptotic stability of an (controlled) equilibrium for EDMD-based model predictive control, in which the optimization step is conducted using the data-based surrogate model. To this end, we derive novel bounds on the estimation error that are proportional to the norm of state and control. This enables us to show that, if the underlying system is cost controllable, this stabilizablility property is preserved. We conduct numerical simulations illustrating the proven practical asymptotic stability.