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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.15063 |
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| _version_ | 1866918395059896320 |
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| author | Quartullo, Renato Garulli, Andrea Leomanni, Mirko |
| author_facet | Quartullo, Renato Garulli, Andrea Leomanni, Mirko |
| contents | This paper presents a new data-driven robust predictive control law, for linear systems affected by unknown-but-bounded process disturbances. A sequence of input-state data is used to construct a suitable uncertainty representation based on interval matrices. Then, the effect of uncertainty along the prediction horizon is bounded through an operator leveraging matrix zonotopes. This yields a tube that is exploited within a variable-horizon optimal control problem, to guarantee robust satisfaction of state and input constraints. The resulting data-driven predictive control scheme is proven to be recursively feasible and practically stable. A numerical example shows that the proposed approach compares favorably to existing methods based on zonotopic tubes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15063 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-Driven Robust Predictive Control with Interval Matrix Uncertainty Propagation Quartullo, Renato Garulli, Andrea Leomanni, Mirko Systems and Control This paper presents a new data-driven robust predictive control law, for linear systems affected by unknown-but-bounded process disturbances. A sequence of input-state data is used to construct a suitable uncertainty representation based on interval matrices. Then, the effect of uncertainty along the prediction horizon is bounded through an operator leveraging matrix zonotopes. This yields a tube that is exploited within a variable-horizon optimal control problem, to guarantee robust satisfaction of state and input constraints. The resulting data-driven predictive control scheme is proven to be recursively feasible and practically stable. A numerical example shows that the proposed approach compares favorably to existing methods based on zonotopic tubes. |
| title | Data-Driven Robust Predictive Control with Interval Matrix Uncertainty Propagation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2603.15063 |