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

Abstract

High-dimensional high-frequency continuous-vibration control problems often have very complex dynamic behaviors. It is difficult for the conventional control methods to obtain appropriate control laws from such complex systems to suppress the vibration. This paper proposes a new vibration controller by using reinforcement learning (RL) and a finite-impulse-response (FIR) filter. First, a simulator with enough physical fidelity was built for the vibration system. Then, the deep deterministic policy gradient (DDPG) algorithm interacted with the simulator to find a near-optimal control policy to meet the specified goals. Finally, the control policy, represented as a neural network, was run directly on a controller in real-world experiments with high-dimensional and high-frequency dynamics. The simulation results show that the maximum peak values of the power-spectrum-density (PSD) curves at specific frequencies can be reduced by over 63%. The experimental results show that the peak values of the PSD curves at specific frequencies were reduced by more than 47% (maximum over 52%). The numerical and experimental results indicate that the proposed controller can significantly attenuate various vibrations within the range from 50 Hz to 60 Hz.

Keywords

• active vibration control
• reinforcement learning
• deep deterministic policy gradient (DDPG)
• neural network
• finite-impulse-response filter
• experiment