{"ID":2859486,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06153","arxiv_id":"2510.06153","title":"Robust Data-Driven Receding Horizon Control","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.06153","url_pdf":"https://arxiv.org/pdf/2510.06153v1","authors":"[\"Jian Zheng\",\"Sahand Kiani\",\"Mario Sznaier\",\"Constantino Lagoa\"]","published":"2025-10-07T17:22:29Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}