基于改进蜘蛛猴算法的永磁同步电机多参数辨识

Multi-parameter Identification of Permanent Magnet Synchronous Motor based on Chaos Spider Monkey Optimization Algorithm

为解决一类智能算法对永磁同步电机(PMSM)参数辨识存在不稳定和精度不高的问题,现提出一种基于改进Tent混沌映射的反向学习蜘蛛猴优化(CSMO)算法。首先,采用改进的Tent混沌映射产生多样性强的初始种群,保证搜索空间的遍历性;其次,在局部领导阶段使用自适应步长取代随机步长,使其适应不同阶段的搜索,有效平衡了全局探索能力与局部开发能力;然后,在局部领导者决策阶段采用柯西变异策略使个体跳出局部最优,从而进一步加强了群体搜索能力,有助于在当前解的局部区域中发现较优解,使得算法的探索速度和寻优精度得到平衡;最后,使用标准测试函数来检测CSMO算法的性能,建立用于PMSM参数辨识的数学模型,再将该模型应用在改进的算法中来辨识电机参数。仿真与实验分析表明,CSMO算法对永磁同步电机参数的辨识有着较好的精度和收敛速度。

To address the instability and low accuracy of intelligent algorithms for parameter identification of permanent magnet synchronous motors(PMSM),a reverse learning spider monkey optimization algorithm based on improved Tent chaotic map is proposed.Initially,an improved Tent chaotic map is used to generate a diverse initial population,ensuring diversity in the search space.Subsequently,an exponential decreasing step size is employed in the local leader phase to replace the random step size,enabling adaptation to different search stages and effectively balancing global exploration capability with local exploitation ability.Furthermore,a Cauchy mutation strategy is utilized in the local leader decision phase to enable individuals to escape local optima,thereby further enhancing group search capability and facilitating the discovery of optimal solutions in the current solution space region.Finally,standard test functions are used to evaluate the performance of the CSMO algorithm,and a mathematical model for PMSM parameter identification is established and applied in the improved algorithm for parameter identification of the motor.Simulation and experimental analysis show that the CSMO algorithm has good accuracy and convergence speed for the identification of permanent magnet synchronous motor parameters.