{"ID":2894815,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.10166","arxiv_id":"2507.10166","title":"Recursive Feasibility without Terminal Constraints via Parent-Child MPC Architecture","abstract":"This paper proposes a novel hierarchical model predictive control (MPC) framework, called the Parent-Child MPC architecture, to steer nonlinear systems under uncertainty towards a target set, balancing computational complexity and guaranteeing recursive feasibility and stability without relying on conservative terminal constraints in online decision-making. By coupling a small-horizon Child MPC layer with one or more large-horizon Parent MPC layers, the architecture ensures recursive feasibility and stability through adjustable stage-wise constraints derived from tube-based control. As is demonstrated in our case studies, compared to traditional MPC methods, the proposed Parent-Child MPC architecture enhances performance and computational efficiency, reduces conservativeness, and enables scalable planning for certain nonlinear systems.","short_abstract":"This paper proposes a novel hierarchical model predictive control (MPC) framework, called the Parent-Child MPC architecture, to steer nonlinear systems under uncertainty towards a target set, balancing computational complexity and guaranteeing recursive feasibility and stability without relying on conservative terminal...","url_abs":"https://arxiv.org/abs/2507.10166","url_pdf":"https://arxiv.org/pdf/2507.10166v1","authors":"[\"Filip Surma\",\"Anahita Jamshidnejad\"]","published":"2025-07-14T11:26:59Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}