{"ID":2867010,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18760","arxiv_id":"2509.18760","title":"Guaranteed Robust Nonlinear MPC via Disturbance Feedback","abstract":"Robots must satisfy safety-critical state and input constraints despite disturbances and model mismatch. We introduce a robust model predictive control (RMPC) formulation that is fast, scalable, and compatible with real-time implementation. Our formulation guarantees robust constraint satisfaction, input-to-state stability (ISS) and recursive feasibility. The key idea is to decompose the uncertain nonlinear system into (i) a nominal nonlinear dynamic model, (ii) disturbance-feedback controllers, and (iii) bounds on the model error. These components are optimized jointly using sequential convex programming. The resulting convex subproblems are solved efficiently using a recent disturbance-feedback MPC solver. The approach is validated across multiple dynamics, including a rocket-landing problem with steerable thrust. An open-source implementation is available at https://github.com/antoineleeman/robust-nonlinear-mpc.","short_abstract":"Robots must satisfy safety-critical state and input constraints despite disturbances and model mismatch. We introduce a robust model predictive control (RMPC) formulation that is fast, scalable, and compatible with real-time implementation. Our formulation guarantees robust constraint satisfaction, input-to-state stabi...","url_abs":"https://arxiv.org/abs/2509.18760","url_pdf":"https://arxiv.org/pdf/2509.18760v1","authors":"[\"Antoine P. Leeman\",\"Johannes Köhler\",\"Melanie N. Zeilinger\"]","published":"2025-09-23T07:54:20Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false,"code_links":[{"ID":609426,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2867010,"paper_url":"https://arxiv.org/abs/2509.18760","paper_title":"Guaranteed Robust Nonlinear MPC via Disturbance Feedback","repo_url":"https://github.com/antoineleeman/robust-nonlinear-mpc","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}