A Computationally Efficient Robust Model Predictive Control Framework for Uncertain Nonlinear Systems

• 成果类型: Article
• 署名作者: Koehler, Johannes; Soloperto, Raffaele; Mueller, Matthias A.; Allgoewer, Frank
• 署名单位: University of Stuttgart; Leibniz University Hannover
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2982585
• 发表日期: 2021
• 页码: 794-801
• 关键词: Nonlinear model predictive control (MPC); robust MPC; Constrained control; uncertain systems

摘要

In this article, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal (state and input) constraints. To facilitate an efficient online implementation, the shape of the tube is based on an offline computed incremental Lyapunov function with a corresponding (nonlinear) incrementally stabilizing feedback. Crucially, the online optimization only implicitly includes these nonlinear functions in terms of scalar bounds, which enables an efficient implementation. Furthermore, to account for an efficient evaluation of the worst case disturbance, a simple function is constructed offline that upper bounds the possible disturbance realizations in a neighborhood of a given point of the open-loop trajectory. The resulting MPC scheme ensures robust constraint satisfaction and practical asymptotic stability with a moderate increase in the online computational demand compared to a nominal MPC. We demonstrate the applicability of the proposed framework in comparison to state-of-the-art robust MPC approaches with a nonlinear benchmark example.