This paper addresses the challenge of designing an automatic train driving controller for high-speed railway systems to improve the accuracy and stability of train speed control. The Automatic Train Operation (ATO) system is crucial for ensuring safety, punctuality, and comfort. Traditional control algorithms, such as PID, Sliding Mode Control (SMC), and neural networks, have limitations in handling the nonlinear and time-varying nature of train dynamics. To overcome these, we propose a PID-type Iterative Learning Control (ILC) algorithm that leverages the repetitive characteristics of train operations. The algorithm combines feedback and feedforward mechanisms to process operational data iteratively, enabling rapid and accurate tracking of the target speed curve. Simulations using real track data and train parameters validate the algorithm's effectiveness, demonstrating improved tracking accuracy and convergence as the number of iterations increases. The PID-type ILC algorithm outperforms traditional PID control, showing its potential for high-precision, fast-response, and stable tracking in automatic train operation systems.