Permanent magnet synchronous motors (PMSM) generate inherent torque ripples due to non-ideal structural characteristics, magnetic saturation, cogging effects, and power electronic drive nonlinearities. These ripples induce mechanical vibrations, high-frequency noise, and degrade control precision and operational stability, ultimately limiting PMSM performance in high-precision applications.To overcome the limitations of conventional iterative learning control (ILC) in handling non-periodic disturbances and parameter tuning difficulties, this paper develops a suppression strategy based on parameter-adaptive iterative learning control (PA-ILC) method. The proposed controller enables real-time parameter self-adjustment, significantly enhancing periodic torque ripple suppression. By integrating an adaptive algorithm for non-periodic ripple compensation, a composite control strategy is established.Validation through MATLAB/Simulink simulations and hardware-in-the-loop experiments demonstrates that compared to traditional PI control and fixed-parameter ILC, the proposed method reduces torque ripple amplitude while improving tracking accuracy and system robustness.