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

Henrique Raduenz, Liselott Ericson, Victor J. De Negri and Petter Krus
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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

Energies, 2022, vol. 15, issue 14, 1-16

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.

Keywords: reinforcement learning; multi-chamber actuator; mode selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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