Abstract: To address the shortcomings of train speed tracking algorithms based on proportional integral derivative (PID) control in tracking progress, convergence, and stability, this paper oriposed a PID based iterative learning control (ILC) based automatic train operation (ATO) curve tracking algorithm. The paper optimized the tracking process through iterative learning control, reduced tracking errors, and shorten convergence time, set up typical scenarios to conduct simulation experiments on the designed algorithm, and compared and analyzed the simulation results with the tracking effect of the PID control algorithm. The results show that the PID ILC algorithm has high tracking accuracy for train target speed and target displacement, and can achieve precise tracking within a limited number of iterations, proving the effectiveness of the proposed algorithm.