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Controlling a Hydraulic System using Reinforcement Learning: Implementation and validation of a DQN-agent on a hydraulic Multi-Chamber cylinder system
Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems.
Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems.
2021 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

One of the largest energy losses in an excavator is the compensation loss. In a hydraulic load sensing system where one pump supplies multiple actuators, these compensation losses are inevitable. To minimize the compensation losses the use of a multi chamber cylinder can be used, which can control the load pressure by activate its chambers in different combinations and in turn minimize the compensation losses. 

For this proposed architecture, the control of the multi chamber cylinder systems is not trivial. The possible states of the system, due to the number of combinations, makes conventional control, like a rule based strategy, unfeasible. Therefore, is the reinforcement learning a promising approach to find an optimal control. 

A hydraulic system was modeled and validated against a physical one, as a base for the reinforcement learning to learn in simulation environment. A satisfactory model was achieved, accurately modeled the static behavior of the system but lacks some dynamics. 

A Deep Q-Network agent was used which successfully managed to select optimal combinations for given loads when implemented in the physical test rig, even though the simulation model was not perfect.

Place, publisher, year, edition, pages
2021. , p. 67
Keywords [en]
Gear selection, reinforcement learning, machine learning, DQN, neural network, multi chamber cylinder, Digital hydraulics, hydraulics, Matlab, Simulink, Hopsan, hydraulic system validation
National Category
Other Mechanical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-177216ISRN: LIU-IEI-TEK-A--21/04015–SEOAI: oai:DiVA.org:liu-177216DiVA, id: diva2:1571659
External cooperation
Volvo CE
Subject / course
Fluid and Mechanical Engineering Systems
Presentation
2021-06-09, Online, Linköping, 22:47 (English)
Supervisors
Examiners
Available from: 2021-06-23 Created: 2021-06-22 Last updated: 2021-06-23Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf