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Multi-Chamber Actuator Mode Selection through Reinforcement Learning–Simulations and Experiments

Author

Listed:
  • Henrique Raduenz

    (Division of Fluid and Mechatronics Systems, Linköping University, 581 83 Linköping, Sweden)

  • Liselott Ericson

    (Division of Fluid and Mechatronics Systems, Linköping University, 581 83 Linköping, Sweden)

  • Victor J. De Negri

    (Laboratory of Hydraulic and Pneumatic Systems, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Petter Krus

    (Division of Fluid and Mechatronics Systems, Linköping University, 581 83 Linköping, Sweden)

Abstract

This paper presents the development and implementation of a reinforcement learning agent as the mode selector for a multi-chamber actuator in a load-sensing architecture. The agent selects the mode of the actuator to minimise system energy losses. The agent was trained in a simulated environment and afterwards deployed to the real system. Simulation results indicated the capability of the agent to reduce energy consumption, while maintaining the actuation performance. Experimental results showed the capability of the agent to learn via simulation and to control the real system.

Suggested Citation

  • Henrique Raduenz & Liselott Ericson & Victor J. De Negri & Petter Krus, 2022. "Multi-Chamber Actuator Mode Selection through Reinforcement Learning–Simulations and Experiments," Energies, MDPI, vol. 15(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5117-:d:862092
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    References listed on IDEAS

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    1. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
    2. Milos Vukovic & Roland Leifeld & Hubertus Murrenhoff, 2017. "Reducing Fuel Consumption in Hydraulic Excavators—A Comprehensive Analysis," Energies, MDPI, vol. 10(5), pages 1-25, May.
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