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Research on Vibration Reduction Control Based on Reinforcement Learning
Magnetorheological (MR) dampers, as an intelligent vibration damping device, can quickly change the damping size of the material in milliseconds. The traditional semiactive control strategy cannot give full play to the ability of the MR dampers to consume energy and reduce vibration under different currents, and it is difficult to control the MR dampers accurately. In this paper, a semiactive control strategy based on reinforcement learning (RL) is proposed, which is based on “exploring” to learn the optimal value of the MR dampers at each step of the operation, the applied current value. During damping control, the learned optimal action value for each step is input into the MR dampers so that they provide the optimal damping force to the structure. Applying this strategy to a two-layer frame structure was found to provide more accurate control of the MR dampers, significantly improving the damping effect of the MR dampers.
Research on Vibration Reduction Control Based on Reinforcement Learning
Magnetorheological (MR) dampers, as an intelligent vibration damping device, can quickly change the damping size of the material in milliseconds. The traditional semiactive control strategy cannot give full play to the ability of the MR dampers to consume energy and reduce vibration under different currents, and it is difficult to control the MR dampers accurately. In this paper, a semiactive control strategy based on reinforcement learning (RL) is proposed, which is based on “exploring” to learn the optimal value of the MR dampers at each step of the operation, the applied current value. During damping control, the learned optimal action value for each step is input into the MR dampers so that they provide the optimal damping force to the structure. Applying this strategy to a two-layer frame structure was found to provide more accurate control of the MR dampers, significantly improving the damping effect of the MR dampers.
Research on Vibration Reduction Control Based on Reinforcement Learning
Rongyao Yuan (Autor:in) / Yang Yang (Autor:in) / Chao Su (Autor:in) / Shaopei Hu (Autor:in) / Heng Zhang (Autor:in) / Enhua Cao (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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