Offset-Free Model Predictive Control: Stability Under Plant-Model Mismatch

• 成果类型: Article
• 署名作者: Kuntz, Steven J.; Rawlings, James B.
• 署名单位: University of California System; University of California Santa Barbara
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3637102
• 发表日期: 2026
• 关键词: NONLINEAR MPC; input; optimization

摘要

We present the first general stability results for nonlinear offset-free model predictive control (MPC). Despite over 20 years of active research, the offset-free MPC literature has not shaken the assumption of closed-loop stability for establishing offset-free performance. In this article, we present a nonlinear offset-free MPC design that is robustly stable with respect to the tracking errors, and thus achieves offset-free performance, despite the plant-model mismatch and persistent disturbances. Key features and assumptions of this design include quadratic costs, differentiability of the plant and model functions, constraint backoffs at steady state, and a robustly stable state and disturbance estimator. We first establish nominal stability and offset-free performance. Then, robustness to state and disturbance estimate errors and setpoint and disturbance changes is demonstrated. Finally, the results are extended to sufficiently small plant-model mismatch. The results are illustrated in a nonlinear reactor example.