Schurig, Roland ; Himmel, Andreas ; Findeisen, Rolf (2024)
Geometric Data-Driven Dimensionality Reduction in MPC with Guarantees.
22nd European Control Conference. Stockholm, Sweden (25.06.2024-28.06.2024)
doi: 10.23919/ECC64448.2024.10591254
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We address the challenge of dimension reduction in the discrete-time optimal control problem which is solved repeatedly online within the framework of model predictive control. Our study demonstrates that a reduced-order approach, aimed at identifying a suboptimal solution within a low-dimensional subspace, retains the stability and recursive feasibility characteristics of the original problem. We present a necessary and sufficient condition for ensuring initial feasibility, which is seamlessly integrated into the subspace design process. Additionally, we employ techniques from optimization on Riemannian manifolds to develop a subspace that efficiently represents a collection of pre-specified high-dimensional data points, all while adhering to the initial admissibility constraint.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| 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: | 24 Juli 2024 |
| Verlag: | IEEE |
| Buchtitel: | 2024 European Control Conference (ECC 2024) |
| Veranstaltungstitel: | 22nd European Control Conference |
| Veranstaltungsort: | Stockholm, Sweden |
| Veranstaltungsdatum: | 25.06.2024-28.06.2024 |
| DOI: | 10.23919/ECC64448.2024.10591254 |
| Kurzbeschreibung (Abstract): | We address the challenge of dimension reduction in the discrete-time optimal control problem which is solved repeatedly online within the framework of model predictive control. Our study demonstrates that a reduced-order approach, aimed at identifying a suboptimal solution within a low-dimensional subspace, retains the stability and recursive feasibility characteristics of the original problem. We present a necessary and sufficient condition for ensuring initial feasibility, which is seamlessly integrated into the subspace design process. Additionally, we employ techniques from optimization on Riemannian manifolds to develop a subspace that efficiently represents a collection of pre-specified high-dimensional data points, all while adhering to the initial admissibility constraint. |
| Freie Schlagworte: | emergenCITY_CPS, emergenCITY |
| 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) LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY |
| Hinterlegungsdatum: | 17 Dez 2024 12:47 |
| Letzte Änderung: | 17 Dez 2024 12:51 |
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