Design and Applications of Q-Learning Adaptive PID Algorithm for Maglev Train Levitation Control System

To improve the levitation stability of the maglev train, an adaptive PID controller based on Q-learning proposed. in particular, the parameters of the traditional PI D are trained through the Q-learning algorithm where three Q tables for the PID parameters are obtained. When the maglev train runs in different operating conditions, the controller parameters can be adaptively and efficiently selected according to the Q tables. The performance of the proposed Q-learning based PID controller is verified by comparing it with the traditional PID and the experimental results show that the proposed PID controller via Q-learning has favorable features on rapidity and stability. Compared with the traditional PID controller, its overshoot is reduced by 0.19%, and the adjustment time is shortened about 32.25%, during the transient levitation process.