空间天线驱动单元控制器的精确设计依赖于永磁同步电机电气参数的准确获取，高精度的电气参数辨识是电机参数可靠获取的基础。针对标准蛇优化算法在永磁同步电机电气参数辨识时存在的收敛速度较慢、辨识精度不高、易陷入局部最优等缺点，引入Tent混沌映射与准反向学习策略增强初始蛇群多样性，改进食物量与环境温度阈值提高算法收敛速度，利用柯西变异布谷鸟搜索算法提升算法全局优化搜索能力及鲁棒性，形成了一种改进蛇优化算法。利用提出的改进蛇优化算法，对某空间天线驱动单元中的永磁同步电机进行电气参数辨识。结果表明，相较于标准蛇优化算法，改进蛇优化算法具有更高的辨识精度、更快的收敛速度和更好的鲁棒性。

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.

TM351