The precise design of the drive unit controller for space antennas depends on the accurate identification of the electrical parameters of the permanent magnet synchronous motor(PMSM). Achieving reliable parameters through precise identification is essential for the motor’s performance. However, the standard snake optimization algorithm(SOA) used in PMSM parameters identification faces several issues, such as slow convergence speed, low accuracy, and susceptibility to local optima. To address these limitations, three strategies are proposed in this paper. First, the Tent chaotic map and quasi-opposition-based learning strategy are introduced to enrich the diversity of the initial snake population. Second, improvements to the thresholds for food quantity and environmental temperature are made to enhance the algorithm’s convergence speed. Finally, the cuckoo search algorithm based on Cauchy mutation is utilized to improve the global optimization capabilities and robustness of the algorithm. These three strategies, combined with the standard SOA, form an improved snake optimization algorithm. The proposed algorithm is applied to identify the electrical parameters of the PMSM in the space antenna drive unit. Results show that, compared with the standard SOA, the improved algorithm achieves higher identification accuracy, faster convergence speed, and better robustness.