Alsmeier, Hendrik ; Savchenko, Anton ; Findeisen, Rolf (2024)
Neural Horizon Model Predictive Control - Increasing Computational Efficiency with Neural Networks.
2024 American Control Conference (ACC). Toronto, Canada (08.07.2024 - 12.07.2024)
doi: 10.23919/ACC60939.2024.10644452
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The expansion in automation of increasingly fast applications and low-power edge devices poses a particular challenge for optimization based control algorithms, like model predictive control. Our proposed machine-learning supported approach addresses this by utilizing a feed-forward neural net-work to reduce the computation load of the online-optimization. We propose approximating part of the problem horizon, while maintaining safety guarantees - constraint satisfaction - via the remaining optimization part of the controller. The approach is validated in simulation, demonstrating an improvement in computational efficiency, while maintaining guarantees and near-optimal performance. The proposed MPC scheme can be applied to a wide range of applications, including those requiring a rapid control response, such as robotics and embedded applications with limited computational resources.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Alsmeier, Hendrik ; Savchenko, Anton ; Findeisen, Rolf |
| Art des Eintrags: | Bibliographie |
| Titel: | Neural Horizon Model Predictive Control - Increasing Computational Efficiency with Neural Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 5 September 2024 |
| Verlag: | IEEE |
| Buchtitel: | 2024 American Control Conference |
| Veranstaltungstitel: | 2024 American Control Conference (ACC) |
| Veranstaltungsort: | Toronto, Canada |
| Veranstaltungsdatum: | 08.07.2024 - 12.07.2024 |
| DOI: | 10.23919/ACC60939.2024.10644452 |
| Kurzbeschreibung (Abstract): | The expansion in automation of increasingly fast applications and low-power edge devices poses a particular challenge for optimization based control algorithms, like model predictive control. Our proposed machine-learning supported approach addresses this by utilizing a feed-forward neural net-work to reduce the computation load of the online-optimization. We propose approximating part of the problem horizon, while maintaining safety guarantees - constraint satisfaction - via the remaining optimization part of the controller. The approach is validated in simulation, demonstrating an improvement in computational efficiency, while maintaining guarantees and near-optimal performance. The proposed MPC scheme can be applied to a wide range of applications, including those requiring a rapid control response, such as robotics and embedded applications with limited computational resources. |
| 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: | 06 Nov 2024 13:06 |
| Letzte Änderung: | 06 Nov 2024 13:06 |
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