Abstract: To address the difficulties in parameter tuning of Active Disturbance Rejection Control (ADRC) and the insufficient tracking and disturbance rejection performance of Permanent Magnet Synchronous Motors (PMSMs) under complex operating conditions, an ADRC parameter optimization method based on a multi-strategy fusion improved Whale Optimization Algorithm (MAFWOA) is proposed. Elite opposition-based learning, a nonlinear convergence factor, a golden sine strategy, and random differential perturbation are introduced to enhance the global search capability and optimization accuracy of the algorithm. Standard benchmark functions and motor experiments are employed to verify the effectiveness of the proposed method. The results show that MAFWOA achieves faster convergence and higher optimization accuracy. Moreover, the optimized ADRC exhibits superior performance in position tracking and load disturbance suppression. The study demonstrates that the proposed MAFWOA-ADRC control strategy can effectively improve the tracking accuracy and disturbance rejection capability of PMSMs.