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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:71076 |
| Date | 23 June 2020 |
| Creators | Kreutmayr, Fabian, Imlauer, Markus |
| Contributors | Dresdner Verein zur Förderung der Fluidtechnik e. V. Dresden |
| Publisher | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.25368/2020.6, urn:nbn:de:bsz:14-qucosa2-709160, qucosa:70916 |
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