Adaptive Economic Model Predictive Control: Performance Guarantees for Nonlinear Systems

• 成果类型: Article
• 署名作者: Degner, Maximilian; Soloperto, Raffaele; Zeilinger, Melanie N.; Lygeros, John; Kohler, Johannes
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Stuttgart; Swiss Federal Institutes of Technology Domain; ETH Zurich; Imperial College London
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3655254
• 发表日期: 2026
• 关键词: ROBUST MPC; operation

摘要

We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular, we propose an adaptive economic model predictive control framework that: 1) directly minimizes transient economic costs; 2) addresses parameteric uncertainty through online model adaptation; and 3) determines optimal setpoints online, and fourth, ensures robustness by using a tube-based approach. The proposed design ensures recursive feasibility, robust constraint satisfaction, and a transient performance bound. In case the disturbances have a finite energy and the parameter variations have a finite path length, the asymptotic average performance is (approximately) not worse than the performance obtained when operating at the best reachable steady-state. We highlight performance benefits in a numerical example involving a chemical reactor with unknown time-invariant and time-varying parameters.