A Successive Convexification Approach for Robust Receding Horizon Control

• 成果类型: Article
• 署名作者: Lishkova, Yana; Cannon, Mark
• 署名单位: University of Oxford
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3558253
• 发表日期: 2025
• 页码: 6436-6448
• 关键词: trajectory; optimal control; Convex functions; CONVERGENCE; stability analysis; computational modeling; nonlinear dynamical systems; Additives; training; Numerical stability; convex programming; Nonlinear systems; receding horizon control; Robust control

摘要

A novel robust nonlinear model predictive control strategy is proposed for systems with nonlinear dynamics and convex state and control constraints. Using a sequential convex approximation approach and a difference of convex functions representation, the scheme constructs tubes that contain predicted model trajectories, accounting for approximation errors and disturbances, and guaranteeing constraint satisfaction. An optimal control problem is solved as a sequence of convex programs. We develop the scheme initially in the absence of external disturbances and show that the proposed nominal approach is nonconservative, with the solutions of successive convex programs converging to a locally optimal solution for the original optimal control problem. We extend the approach to the case of additive disturbances using a novel strategy for selecting linearization points. As a result, we formulate a robust receding horizon strategy with guarantees of recursive feasibility and closed-loop stability.