基于改进SPBO算法的水下推进永磁同步电机参数辨识

精确的永磁同步电机(permanent magnet synchronous motor, PMSM)模型是集成电机水下推进器恒转矩控制的关键。为了提高水下推进PMSM参数辨识的精度，提出一种融合Tent映射与t分布变异策略的改进学生心理优化算法辨识水下推进PMSM参数。该算法在标准学生心理优化算法的基础上，引入Tent映射策略增加初始种群多样性，引入t分布变异策略平衡算法的前期与后期搜索能力，提高了算法的收敛速度和收敛精度。仿真和实验结果表明：该算法的辨识误差可达1%以内，且具有更快的收敛速度和更好的辨识稳定性。

Parameter Identification of Underwater Propulsion PMSM Based on Improved SPBO

Accurate permanent magnet synchronous motor (PMSM) model is the key to the constant torque control of the integrated motor underwater thruster. In order to improve the identification accuracy of underwater propulsion PMSM parameters, an improved student psychological based optimization algorithm that combines Tent map and t distribution variation strategy was proposed to identify underwater propulsion PMSM parameters. Based on the standard student psychology based algorithm, the Tent map strategy was introduced the to increase the diversity of the initial population, and the t distribution mutation strategy was introduced to balance the early and late search capabilities of the algorithm, which improved the convergence speed and accuracy of the algorithm. The simulation and experimental results show that the identification error of the algorithm can reach less than 1%, and it has faster convergence speed and better identification stability.