Design of magnetic levitation self-learning control system based on Q-network

Aiming at the existing methods of magnetic levitation control system dependent on the dynamic model, the reinforcement learning method was adopted to train the self-learning controller of magnetic levitation system using the Q-network without the system model. The reward function was designed based on the motion direction of the system to improve the convergence speed of training process. A training algorithm based on system weighted average states (WAS) was proposed to adaptively adjust the number of training steps in each episode to extend the control range of the control network. The numerical simulation results show that the Q-network self-learning controller from the improved algorithm can stabilize the magnetic levitation system. Compared with the general reinforcement learning algorithm, the Q-network self-learning controller derived from the WAS algorithm can achieve a wider range of stability control. © 2021, Harbin University of Science and Technology Publication. All right reserved.