Robust Adaptive NMPC Using Ellipsoidal Tubes
成果类型：
Article
署名作者：
Buerger, Johannes; Cannon, Mark
署名单位：
BMW AG; University of Oxford
刊物名称：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
ISSN/ISSBN：
0018-9286
DOI：
10.1109/TAC.2026.3672069
发表日期：
2026
关键词：
摘要：
We propose a computationally efficient nonlinear model predictive control algorithm for safe, learning-based control. The system model is represented as an affine combination of basis functions with unknown parameters, and is subject to additive set-bounded disturbances. Our algorithm employs successive linearization around nominal predicted trajectories and accounts for uncertainties in predicted states due to linearization, model errors, and disturbances using ellipsoidal sets. The ellipsoidal tube-based approach ensures that constraints on control inputs and system states are satisfied. Robustness to uncertainty is ensured using bounds on linearization errors and a backtracking line search. We show that the ellipsoidal embedding of model uncertainty scales favorably with system dimensions in numerical simulations. The algorithm incorporates set membership parameter estimation and provides guarantees of recursive feasibility and input-to-state practical stability.