Guaranteed Robust Nonlinear MPC via Disturbance Feedback
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916963997974528 |
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| author | Leeman, Antoine P. Köhler, Johannes Zeilinger, Melanie N. |
| author_facet | Leeman, Antoine P. Köhler, Johannes Zeilinger, Melanie N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18760 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Guaranteed Robust Nonlinear MPC via Disturbance Feedback Leeman, Antoine P. Köhler, Johannes Zeilinger, Melanie N. Optimization and Control Robotics Systems and Control 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. |
| title | Guaranteed Robust Nonlinear MPC via Disturbance Feedback |
| topic | Optimization and Control Robotics Systems and Control |
| url | https://arxiv.org/abs/2509.18760 |