- title
- Application of machine learning to improve to performance of a pressure-controlled system
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-710761
- conference
- 12th International Fluid Power Conference (12. IFK). Dresden, October 12 – 14, 2020
- original_in_proceeding0
- 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.
- Keywords (DE)
- 12. IFK, Hydraulische Steuerungssysteme, Maschinelles Lernen, Verstärkungslernen
- Keywords (EN)
- 12th International Fluid Power Conference, Hydraulic control systems, Machine learning, Reinforcement learning
- Classification (DDC)
- 620
- Classification (RVK)
- ZQ 5460
- university_publisher
- Technische Universität Dresden, Dresden
- corporation_other
- Dresdner Verein zur Förderung der Fluidtechnik e. V. Dresden, Dresden
- version
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-710761
- Qucosa date of publication
- 23.06.2020
- Document type
- in_proceeding
- Document language
- English
- licence
