Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 17 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 27 Sept. 2024 |
Abstract
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2024.
Publikation: Arbeitspapier/Preprint › Arbeitspapier/Diskussionspapier
}
TY - UNPB
T1 - Robust and efficient data-driven predictive control
AU - Alsalti, Mohammad Salahaldeen Ahmad
AU - Barkey, Manuel
AU - Lopez Mejia, Victor Gabriel
AU - Müller, Matthias A.
PY - 2024/9/27
Y1 - 2024/9/27
N2 - We propose a robust and efficient data-driven predictive control (eDDPC) scheme which is more sample efficient (requires less offline data) compared to existing schemes, and is also computationally efficient. This is done by leveraging an alternative data-based representation of the trajectories of linear time-invariant (LTI) systems. The proposed scheme relies only on using (short and potentially irregularly measured) noisy input-output data, the amount of which is independent of the prediction horizon. To account for measurement noise, we provide a novel result that quantifies the uncertainty between the true (unknown) restricted behavior of the system and the estimated one from noisy data. Furthermore, we show that the robust eDDPC scheme is recursively feasible and that the resulting closed-loop system is practically stable. Finally, we compare the performance of this scheme to existing ones on a case study of a four tank system.
AB - We propose a robust and efficient data-driven predictive control (eDDPC) scheme which is more sample efficient (requires less offline data) compared to existing schemes, and is also computationally efficient. This is done by leveraging an alternative data-based representation of the trajectories of linear time-invariant (LTI) systems. The proposed scheme relies only on using (short and potentially irregularly measured) noisy input-output data, the amount of which is independent of the prediction horizon. To account for measurement noise, we provide a novel result that quantifies the uncertainty between the true (unknown) restricted behavior of the system and the estimated one from noisy data. Furthermore, we show that the robust eDDPC scheme is recursively feasible and that the resulting closed-loop system is practically stable. Finally, we compare the performance of this scheme to existing ones on a case study of a four tank system.
U2 - 10.48550/arXiv.2409.18867
DO - 10.48550/arXiv.2409.18867
M3 - Working paper/Discussion paper
BT - Robust and efficient data-driven predictive control
ER -