Parking tracking accuracy directly affects parking safety, efficiency, and available parking space. Currently, most autonomous parking path tracking relies on model-based feedback control. High tracking errors can arise from a decline in the algorithm's control performance due to uncertainties in system model parameters. In this paper, a feedforward control approach based on iterative learning was developed to reduce the impact of model parameter uncertainty on parking path tracking. Considering that iterative learning control of the system in the time domain was usually affected by the actual speed of the actuator, the system was transformed from the time domain to the space domain, which was related to the desired path. Due to the difficulty in measuring some state variables in the system model and the system's failure to meet the D-type iterative learning rate convergence condition, the design criteria for an H_∞ observer were proposed to accurately estimate state information. Meanwhile, an augmented system with observation errors was constructed to implement iterative learning control, which further reduced the parking path tracking error based on the initial parking tracking information from linear quadratic optimal control (LQR). Finally, a hardware-in-the-loop (HIL) test was established, which proved that the proposed method had excellent practical application potential. The experimental results show that after several iterations, the proposed control method tracks the desired path more accurately than the initial LQR control.