BP-MPC: Optimizing the Closed-Loop Performance of MPC Using Backpropagation

• 成果类型: Article
• 署名作者: Zuliani, Riccardo; Balta, Efe C.; Lygeros, John
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3545767
• 发表日期: 2025
• 页码: 5690-5704
• 关键词: Optimization; Jacobian matrices; COSTS; trajectory; backpropagation; nonlinear dynamical systems; system dynamics; predictive models; CONVERGENCE; Tuning; differentiable optimization; model predictive control (MPC); policy optimization

摘要

Model predictive control (MPC) is pervasive in research and industry. However, designing the cost function and the constraints of the MPC to maximize closed-loop performance remains an open problem. To achieve optimal tuning, we propose a backpropagation scheme that solves a policy optimization problem with nonlinear system dynamics and MPC policies. We enforce the system dynamics using linearization and allow the MPC problem to contain elements that depend on the current system state and on past MPC solutions. Moreover, we propose a simple extension that can deal with losses of feasibility. Our approach, unlike other methods in the literature, enjoys convergence guarantees.