- Titel
- Application of machine learning to improve to performance of a pressure-controlled system
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-710761
- Konferenz
- 12th International Fluid Power Conference (12. IFK). Dresden, October 12 – 14, 2020
- Quellenangabe
- Volume 1 – Symposium - 1
Erscheinungsort: Dresden
Verlag: Technische Universität Dresden
Erscheinungsjahr: 2020
Bandnummer Schriftenreihe: 1
Seiten: 77-86
DOI: 10.25368/2020.6 - DOI
- https://doi.org/10.25368/2020.15
- Abstract (EN)
- Due to the robustness and flexibility of hydraulic components, hydraulic control systems are used in a wide range of applications under various environmental conditions. However, the coverage of this broad field of applications often comes with a loss of performance. Especially when conditions and working points change often, hydraulic control systems cannot work at their optimum. Flexible electronic controllers in combination with techniques from the field of machine learning have the potential to overcome these issues. By applying a reinforcement learning algorithm, this paper examines whether learned controllers can compete with an expert-tuned solution. Thereby, the method is thoroughly validated by using simulations and experiments as well.
- Freie Schlagwörter (DE)
- 12. IFK, Hydraulische Steuerungssysteme, Maschinelles Lernen, Verstärkungslernen
- Freie Schlagwörter (EN)
- 12th International Fluid Power Conference, Hydraulic control systems, Machine learning, Reinforcement learning
- Klassifikation (DDC)
- 620
- Klassifikation (RVK)
- ZQ 5460
- Publizierende Institution
- Technische Universität Dresden, Dresden
- Sonstige beteiligte Institution
- Dresdner Verein zur Förderung der Fluidtechnik e. V. Dresden, Dresden
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-710761
- Veröffentlichungsdatum Qucosa
- 23.06.2020
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
