Series Elastic Actuators (SEA) provide improved control, safety, energy efficiency, and performance compared to traditional rigid actuators, making them well-suited for various applications in robotics, rehabilitation, and human-robot interaction. However, the elastic nature of SEAs also introduces significant challenges such as oscillations, reduced trajectory tracking accuracy, and the impact of system uncertainties. This paper proposes Iterative Learning Control (ILC) as an effective solution to address the limitations of SEAs, particularly in improving trajectory tracking accuracy and reducing system oscillations in the absence of precise model information. To ensure system stability when applying ILC, the paper also proposes combining ILC with state feedback and uncertainty compensation, thereby enhancing disturbance rejection and improving trajectory tracking accuracy. Simulation results demonstrate that the proposed method significantly reduces tracking errors and improves system stability compared to traditional control approaches.