Research on Vibration Reduction Control Based on Reinforcement Learning

Abstract

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.

Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks

“…In fact, the use of reinforcement learning methods to construct agents to learn vibration control in complex environments has achieved remarkable results [11][12][13][14][15]. This method has been applied in many fields, such as for suspension vibration control [16,17], manipulator vibration control [18][19][20], magnetorheological damper vibration control [21,22], and other vibration control applications [23][24][25], and has made remarkable progress.…”

“…In the field of magnetorheological damper vibration control [21,22], Park et al [21] proposed a reinforcement learning method for magnetorheological elastomer vibration control based on Q-learning. Their research shows that it is feasible to use a model-free reinforcement learning model to realize an adaptive controller based on the application of highly nonlinear magnetorheological elastomers.…”

A New Vibration Controller Design Method Using Reinforcement Learning and FIR Filters: A Numerical and Experimental Study

“…Actually, RL has been widely used for building agents to learn complex control in complex environments, and it has achieved some successes in a variety of domains [11][12][13][14][15]. Moreover, its applicability has been extended to the vibration-control domain, such as the vibration control of the suspension [16][17][18][19], manipulator [20][21][22], magneto rheological damper [23,24], flexible beam/plate [25][26][27], etc.…”

“…In terms of the vibration-control problems of magneto rheological dampers [23,24], as the controller was designed based on Q-learning [28], they are theoretically not suitable for high-dimensional continuous-action spaces. Park et al [23] proposed a novel reinforcementlearning method based on Q-learning for the vibration control of a magneto rheological elastomer.…”

“…The experimental results showed that the proposed method could minimize the vibration level with respect to the tonal disturbance or sine-sweep disturbance. Yuan et al [24] proposed a semiactive control strategy of a magneto rheological damper based on the Q-learning algorithm, and the simulation results showed that it was better than simple bang-bang control.…”