Schurig, Roland ; Himmel, Andreas ; Findeisen, Rolf (2023)
Geometric Data-Driven Dimensionality Reduction in MPC with
Guarantees.
doi: 10.48550/arXiv.2312.02734
Report, Bibliographie
Kurzbeschreibung (Abstract)
We consider the problem of reducing the dimension of the discrete-time optimal control problem that is solved repeatedly online in model predictive control. We show that a reduced-order scheme, which solves the optimization problem in a low-dimensional subspace, inherits the stability and recursive feasibility properties from the original formulation. We introduce a necessary and sufficient condition for initial feasibility and incorporate that in the subspace design. Finally, we use concepts of optimization over Riemannian manifolds to compute a subspace that provides optimal representations for a set of pre-defined high-dimensional optimizers under the initial admissibility constraint.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Schurig, Roland ; Himmel, Andreas ; Findeisen, Rolf |
| Art des Eintrags: | Bibliographie |
| Titel: | Geometric Data-Driven Dimensionality Reduction in MPC with Guarantees |
| Sprache: | Englisch |
| Publikationsjahr: | 5 Dezember 2023 |
| Verlag: | arXiv |
| Reihe: | Systems and Control |
| Auflage: | 1. Version |
| DOI: | 10.48550/arXiv.2312.02734 |
| URL / URN: | https://arxiv.org/abs/2312.02734 |
| Kurzbeschreibung (Abstract): | We consider the problem of reducing the dimension of the discrete-time optimal control problem that is solved repeatedly online in model predictive control. We show that a reduced-order scheme, which solves the optimization problem in a low-dimensional subspace, inherits the stability and recursive feasibility properties from the original formulation. We introduce a necessary and sufficient condition for initial feasibility and incorporate that in the subspace design. Finally, we use concepts of optimization over Riemannian manifolds to compute a subspace that provides optimal representations for a set of pre-defined high-dimensional optimizers under the initial admissibility constraint. |
| Zusätzliche Informationen: | Preprint |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
| Hinterlegungsdatum: | 08 Dez 2023 13:11 |
| Letzte Änderung: | 31 Jan 2024 10:27 |
| PPN: | 515154415 |
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