{"ID":2859462,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06117","arxiv_id":"2510.06117","title":"Robust Data-Driven Receding-Horizon Control for LQR with Input Constraints","abstract":"This letter presents a robust data-driven receding-horizon control framework for the discrete time linear quadratic regulator (LQR) with input constraints. Unlike existing data-driven approaches that design a controller from initial data and apply it unchanged throughout the trajectory, our method exploits all available execution data in a receding-horizon manner, thereby capturing additional information about the unknown system and enabling less conservative performance. Prior data-driven LQR and model predictive control methods largely rely on Willem's fundamental lemma, which requires noise-free data, or use regularization to address disturbances, offering only practical stability guarantees. In contrast, the proposed approach extends semidefinite program formulations for the data-driven LQR to incorporate input constraints and leverages duality to provide formal robust stability guarantees. Simulation results demonstrate the effectiveness of the method.","short_abstract":"This letter presents a robust data-driven receding-horizon control framework for the discrete time linear quadratic regulator (LQR) with input constraints. Unlike existing data-driven approaches that design a controller from initial data and apply it unchanged throughout the trajectory, our method exploits all availabl...","url_abs":"https://arxiv.org/abs/2510.06117","url_pdf":"https://arxiv.org/pdf/2510.06117v1","authors":"[\"Jian Zheng\",\"Mario Sznaier\"]","published":"2025-10-07T16:52:10Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}