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、更快的收敛速度以及更好的鲁棒性。

The precise design of the drive unit controller of the space antenna depends on the accurate acquisition of the electrical parameters of the permanent magnet synchronous motor. Based on highly precise electrical parameters identification, the motor can obtain reliable parameters. Some shortcomings, such as slow convergence speed, low identification accuracy, and easily falling into local optimum, exist in standard snake optimization algorithm in the electrical parameters identification of permanent magnet synchronous motor. Therefore, three strategies were proposed in the algorithm. Firstly, Tent chaotic map and quasi-opposition-based learning strategy were introduced to enrich the diversity of the initial snake group. Secondly, the threshold of food quantity and en-vironmental temperature were improved to enhance the convergence speed of the algorithm. Finally, cuckoo search algorithm based on Cauchy mutation was utilized to improve the global optimization search ability and robustness of the algorithm. An improved snake optimization algorithm was formed by combining the above three improved strategies and the standard snake optimization algorithm. The electrical parameters of the per-manent magnet synchronous motor in the space antenna drive unit were identified by the improved snake op-timization algorithm. The results show that compared with the standard snake optimization algorithm, the im-proved snake optimization algorithm has higher identification accuracy, faster convergence speed, and better robustness.