# Robust Data-Driven Receding Horizon Control

arXiv: https://arxiv.org/abs/2510.06153 · HTML: https://mlsanity.com/paper/2510.06153/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Safe Vision Language Action Models via Barrier Enhanced Flow Matching](https://mlsanity.com/paper/2607.29569/) (2026)

## Abstract (arXiv)

This paper presents a data-driven receding horizon control framework for discrete-time linear systems that guarantees robust performance in the presence of bounded disturbances. Unlike the majority of existing data-driven predictive control methods, which rely on Willem's fundamental lemma, the proposed method enforces set-membership constraints for data-driven control and utilizes execution data to iteratively refine a set of compatible systems online. Numerical results demonstrate that the proposed receding horizon framework achieves better contractivity for the unknown system compared with regular data-driven control approaches.