Details
| Original language | English |
|---|---|
| Title of host publication | 2024 European Control Conference (ECC) |
| Pages | 84-89 |
| Number of pages | 6 |
| ISBN (electronic) | 978-3-9071-4410-7 |
| Publication status | Published - 2024 |
| Event | 2024 European Control Conference (ECC) - Stockholm, Sweden Duration: 25 Jun 2024 → 28 Jun 2024 |
Abstract
ASJC Scopus subject areas
- Mathematics(all)
- Control and Optimization
- Mathematics(all)
- Modelling and Simulation
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2024 European Control Conference (ECC). 2024. p. 84-89.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Sample- and Computationally Efficient Data-Driven Predictive Control
AU - Alsalti, Mohammad Salahaldeen Ahmad
AU - Barkey, Manuel
AU - Lopez Mejia, Victor Gabriel
AU - Müller, Matthias A.
N1 - Publisher Copyright: © 2024 EUCA.
PY - 2024
Y1 - 2024
N2 - Recently proposed data-driven predictive control schemes for LTI systems use non-parametric representations based on the image of a Hankel matrix of previously collected, persistently exciting, input-output data. Persistence of excitation necessitates that the data is sufficiently long and, hence, the computational complexity of the corresponding finite-horizon optimal control problem increases. In this paper, we propose an efficient data-driven predictive control (eDDPC) scheme which is both more sample efficient (requires less offline data) and computationally efficient (uses less decision variables) compared to existing schemes. This is done by leveraging an alternative data-based representation of the trajectories of LTI systems. We analytically and numerically compare the performance of this scheme to existing ones from the literature.
AB - Recently proposed data-driven predictive control schemes for LTI systems use non-parametric representations based on the image of a Hankel matrix of previously collected, persistently exciting, input-output data. Persistence of excitation necessitates that the data is sufficiently long and, hence, the computational complexity of the corresponding finite-horizon optimal control problem increases. In this paper, we propose an efficient data-driven predictive control (eDDPC) scheme which is both more sample efficient (requires less offline data) and computationally efficient (uses less decision variables) compared to existing schemes. This is done by leveraging an alternative data-based representation of the trajectories of LTI systems. We analytically and numerically compare the performance of this scheme to existing ones from the literature.
UR - http://www.scopus.com/inward/record.url?scp=85200581995&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2309.11238
DO - 10.48550/arXiv.2309.11238
M3 - Conference contribution
SN - 979-8-3315-4092-0
SP - 84
EP - 89
BT - 2024 European Control Conference (ECC)
T2 - 2024 European Control Conference (ECC)
Y2 - 25 June 2024 through 28 June 2024
ER -