Ellipsoidal partitions for improved multi-stage robust model predictive control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915496763326464 |
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| author | Heinlein, Moritz Messerer, Florian Diehl, Moritz Lucia, Sergio |
| author_facet | Heinlein, Moritz Messerer, Florian Diehl, Moritz Lucia, Sergio |
| contents | Ellipsoidal tube-based model predictive control methods effectively account for the propagation of the reachable set, typically employing linear feedback policies. In contrast, scenario-based approaches offer more flexibility in the feedback structure by considering different control actions for different branches of a scenario tree. However, they face challenges in ensuring rigorous guarantees. This work aims to integrate the strengths of both methodologies by enhancing ellipsoidal tube-based MPC with a scenario tree formulation. The uncertainty ellipsoids are partitioned by halfspaces such that each partitioned set can be controlled independently. The proposed ellipsoidal multi-stage approach is demonstrated in a human-robot system, highlighting its advantages in handling uncertainty while maintaining computational tractability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12792 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ellipsoidal partitions for improved multi-stage robust model predictive control Heinlein, Moritz Messerer, Florian Diehl, Moritz Lucia, Sergio Systems and Control Optimization and Control Ellipsoidal tube-based model predictive control methods effectively account for the propagation of the reachable set, typically employing linear feedback policies. In contrast, scenario-based approaches offer more flexibility in the feedback structure by considering different control actions for different branches of a scenario tree. However, they face challenges in ensuring rigorous guarantees. This work aims to integrate the strengths of both methodologies by enhancing ellipsoidal tube-based MPC with a scenario tree formulation. The uncertainty ellipsoids are partitioned by halfspaces such that each partitioned set can be controlled independently. The proposed ellipsoidal multi-stage approach is demonstrated in a human-robot system, highlighting its advantages in handling uncertainty while maintaining computational tractability. |
| title | Ellipsoidal partitions for improved multi-stage robust model predictive control |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2509.12792 |