Learning-based homothetic tube MPC

Conference item

In this paper, we study homothetic tube model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and mixed constraints on the state and input. Different from most existing work on robust MPC, we assume that the true disturbance set is unknown but a conservative surrogate is available a priori. Leveraging the real-time data, we develop an online learning algorithm to approximate the true disturbance set. This approximation and the corresponding constraints in the MPC optimisation are updated online using computationally convenient linear programs. We provide statistical gaps between the true and learned disturbance sets, based on which, probabilistic recursive feasibility of homothetic tube MPC problems is discussed. Numerical simulations are provided to demonstrate the efficacy of our proposed algorithm and compare with state-of-the-art MPC algorithms.

Authors: Gao, Y; Yan, S; Zhou, J; Cannon, MR

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2025 European Control Conference (ECC)
• Pages: 2038-2043
• Publication date: 2025-06-24
• Acceptance date: 2025-04-11
• Event title: 23rd European Control Conference (ECC 2025)
• Event location: Thessaloniki, Greece
• Event website: https://ecc25.euca-ecc.org/
• Event start date: 2025-06-24
• Event end date: 2025-06-27
• DOI: 10.23919/ECC65951.2025.11187061
• EISSN: 2996-8895
• ISSN: 2996-8917
• EISBN: 9783907144121
• ISBN: 9798331502713
• Language: English
• Keywords: tube MPC; constraint satisfaction; data-driven control; learning uncertainty