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Autori principali: Baltussen, Tren, Heemels, Maurice, Katriniok, Alexander
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2511.08542
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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