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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.08542 |
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| _version_ | 1866912708679434240 |
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| author | Baltussen, Tren Heemels, Maurice Katriniok, Alexander |
| author_facet | Baltussen, Tren Heemels, Maurice Katriniok, Alexander |
| contents | This manuscript presents a dual model predictive controller (MPC) that balances the two objectives of dual control, namely, system identification and control. In particular, we propose a Gaussian process (GP)-based MPC that uses the posterior GP covariance for active learning. The dual MPC can steer the system towards states with high covariance, or to the setpoint, thereby balancing system identification and control performance (exploration vs. exploitation). We establish robust constraint satisfaction of the novel dual MPC through a contingency plan. We demonstrate the dual MPC in a numerical study of a nonlinear system with nonparametric uncertainties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual MPC for Active Learning of Nonparametric Uncertainties Baltussen, Tren Heemels, Maurice Katriniok, Alexander Optimization and Control This manuscript presents a dual model predictive controller (MPC) that balances the two objectives of dual control, namely, system identification and control. In particular, we propose a Gaussian process (GP)-based MPC that uses the posterior GP covariance for active learning. The dual MPC can steer the system towards states with high covariance, or to the setpoint, thereby balancing system identification and control performance (exploration vs. exploitation). We establish robust constraint satisfaction of the novel dual MPC through a contingency plan. We demonstrate the dual MPC in a numerical study of a nonlinear system with nonparametric uncertainties. |
| title | Dual MPC for Active Learning of Nonparametric Uncertainties |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2511.08542 |