Robust MPC for Uncertain Linear Systems -- Combining Model Adaptation and Iterative Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911135382372352 |
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| author | Petrenz, Hannes Köhler, Johannes Borrelli, Francesco |
| author_facet | Petrenz, Hannes Köhler, Johannes Borrelli, Francesco |
| contents | This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterative tasks. The approach refines the parameter estimates online using set-membership estimation. Performance enhancement over iterations is achieved by learning the terminal cost from data. Safety is enforced using a terminal set, which is also learned iteratively. The proposed method guarantees recursive feasibility, constraint satisfaction, and a robust bound on the closed-loop cost. Numerical simulations on a mass-spring-damper system demonstrate improved computational efficiency and control performance compared to a robust adaptive MPC scheme without iterative learning of the terminal ingredients. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11261 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust MPC for Uncertain Linear Systems -- Combining Model Adaptation and Iterative Learning Petrenz, Hannes Köhler, Johannes Borrelli, Francesco Systems and Control Optimization and Control This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterative tasks. The approach refines the parameter estimates online using set-membership estimation. Performance enhancement over iterations is achieved by learning the terminal cost from data. Safety is enforced using a terminal set, which is also learned iteratively. The proposed method guarantees recursive feasibility, constraint satisfaction, and a robust bound on the closed-loop cost. Numerical simulations on a mass-spring-damper system demonstrate improved computational efficiency and control performance compared to a robust adaptive MPC scheme without iterative learning of the terminal ingredients. |
| title | Robust MPC for Uncertain Linear Systems -- Combining Model Adaptation and Iterative Learning |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2504.11261 |