A computationally efficient reformulation for Data-Enabled Predictive Control
This work investigates the computational efficiency of Data-EnablEd Predictive Control (DeePC) reformulations. Based on Willems’ fundamental lemma, this control method uses Hankel matrices to represent system dynamics. The size—in particular the number of columns—of these Hankel matrices can incur a significant computational complexity, which has seen several attempts at being reduced. We propose a reformulation of DeePC aiming for lower complexity and show online recursive updates of the data matrix. The method’s effectiveness is illustrated by results obtained in simulation.
Authors
- Stanislav Aranovskiy (ORCID: https://orcid.org/0000-0002-2151-5594)
- Paul Chauchat (ORCID: https://orcid.org/0000-0001-8123-5140)
- Romain Bourdais (ORCID: https://orcid.org/0000-0001-8332-0939)
- Alexandre Faye-Bèdrin (ORCID: https://orcid.org/0000-0003-3821-1869)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Aix-Marseille Université (FR)
- Institut d'Électronique et des Technologies du numéRique (FR)
- CentraleSupélec (FR)
- Laboratoire d’Informatique et Systèmes (FR)
Publication Details
- Published
- 2024-12-16
- DOI
- https://doi.org/10.1109/cdc56724.2024.10885954
- Primary Topic
- Advanced Control Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00