Deep Deterministic Policy Gradient-Based Parameter Selection Method of Notch Filters for Suppressing Mechanical Resonance in Industrial Servo Systems

Tae-ho Oh1, Tae-il Kim1, Ji-seok Han1, Young-seok Kim1, Jee-hyung Lee2, Sang-oh Kim2, Sang-sub Lee2, Sang-hoon Lee2, Dong Il Cho

  • 1Seoul National University
  • 2RS automation



Regular Session


15:30 - 17:30 | Mon 19 Aug | Lau, 6-211 | MoC4

Reinforcement Learning

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This paper presents a parameter selection method of notch filters for suppressing mechanical resonances in industrial servo systems using the deep deterministic policy gradient (DDPG) algorithm. Several methods for tuning the notch filter parameters were studied such as the fast- Fourier-transform-based methods, extended-Kalman-filter- based methods and adaptive notch filter methods. However, these methods do not find the Q parameters of notch filters which play an important role in determining the system stability, and do not consider the cases in which multiple notch filters are required. Deep-Q-network-based method was developed to solve these problems, but the notch filter parameter tuning is limited to discrete action spaces. This paper develops a new parameter selection method of notch filters, using the DDPG algorithm. DDPG algorithm, which is a model-free and actor-critic algorithm using deep neural networks, is utilized for its capability to operate over continuous action spaces. Experiments are performed using an actual industrial servo system to demonstrate that the developed parameter selection method successfully finds the notch filter parameters to suppress the resonances of the system.

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