Rose, Alexander ; Pfefferkorn, Maik ; Nguyen, Hoang Hai ; Findeisen, Rolf (2023)
Learning a Gaussian Process Approximation of a Model Predictive Controller with Guarantees.
62nd IEEE Conference on decision oand Control. Marina Bay Sands, Singapore (13.12.2023-15.12.2023)
doi: 10.1109/CDC49753.2023.10384047
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Model predictive control effectively handles complex dynamical systems with constraints, but its high computational demand often makes real-time application infeasible. We propose using Gaussian process regression to learn an approximation of the controller offline for online use. Our approach incorporates a robust predictive control scheme and provides bounds on approximation errors to ensure recursive feasibility and input-to-state stability. Exploiting a sampling-based scenario approach, we develop an efficient sampling strategy and guarantee that, with high probability, the approximation error remains within acceptable bounds. Our method demonstrates enhanced efficiency and reduced computational demand in an example application.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Rose, Alexander ; Pfefferkorn, Maik ; Nguyen, Hoang Hai ; Findeisen, Rolf |
| Art des Eintrags: | Bibliographie |
| Titel: | Learning a Gaussian Process Approximation of a Model Predictive Controller with Guarantees |
| Sprache: | Englisch |
| Publikationsjahr: | 16 Dezember 2023 |
| Ort: | Marina Bay Sands, Singapore |
| Verlag: | IEEE |
| Buchtitel: | 2023 62nd IEEE Conference on Decision and Control (CDC) |
| Veranstaltungstitel: | 62nd IEEE Conference on decision oand Control |
| Veranstaltungsort: | Marina Bay Sands, Singapore |
| Veranstaltungsdatum: | 13.12.2023-15.12.2023 |
| DOI: | 10.1109/CDC49753.2023.10384047 |
| URL / URN: | https://ieeexplore.ieee.org/document/10384047 |
| Kurzbeschreibung (Abstract): | Model predictive control effectively handles complex dynamical systems with constraints, but its high computational demand often makes real-time application infeasible. We propose using Gaussian process regression to learn an approximation of the controller offline for online use. Our approach incorporates a robust predictive control scheme and provides bounds on approximation errors to ensure recursive feasibility and input-to-state stability. Exploiting a sampling-based scenario approach, we develop an efficient sampling strategy and guarantee that, with high probability, the approximation error remains within acceptable bounds. Our method demonstrates enhanced efficiency and reduced computational demand in an example application. |
| 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: | 28 Feb 2024 09:26 |
| Letzte Änderung: | 29 Mai 2024 10:06 |
| PPN: | 518710300 |
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