Learning-based Homothetic Tube MPC with Non-Asymptotic Guarantees

Changyi Lei; Seth Siriya; Dragan Nešić; Ye Pu

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arXiv · 2607.12343

Learning-based Homothetic Tube MPC with Non-Asymptotic Guarantees

Changyi Lei·Seth Siriya·Dragan Nešić·Ye Pu

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Abstract

This paper studies learning-based MPC for constrained stabilization of discrete-time linear systems with unknown system parameters and additive bounded disturbances. We develop a tractable homothetic-tube MPC scheme in which a high-probability parameter confidence set is generated from non-asymptotic regularized least-squares estimation, rather than assumed a priori. The resulting uncertainty set is embedded into robust tube propagation and constraint tightening, yielding a convex formulation with linear and second-order-cone constraints. We prove high-probability recursive feasibility, robust constraint satisfaction, and input-to-state stability, together with explicit non-asymptotic state bounds. A numerical example illustrates the effectiveness and theoretical guarantees.

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Changyi Lei, Seth Siriya, Dragan Nešić, Ye Pu. 2026-07-14. Learning-based Homothetic Tube MPC with Non-Asymptotic Guarantees. https://arxiv.org/abs/2607.12343

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