摘要
针对一般粒子群算法辨识永磁同步电机参数由于其粒子在迭代后期易陷入局部最优而导致收敛速度慢和辨识精度差的缺陷,提出了一种基于混沌映射和高斯扰动改进的粒子群算法实现对永磁同步电机参数高精度辨识。利用混沌Sine映射构造了一种非线性随机递减惯性权重,并在粒子群的“个体认知”部分引入高斯扰动策略。采用Sine函数构造学习因子。改进算法仅需采集电机定子电流、电压以及转速信号便可实现永磁同步电机多参数的准确辨识。对比仿真结果表明:基于混沌映射和高斯扰动改进的粒子群算法具有更快的收敛速度和更高的辨识精度,对于永磁同步电机控制性能改善具有重要意义。
Aiming at the problem of slow convergence speed and poor identification accuracy caused by the general particle swarm optimization algorithm in parameter estimation of permanent magnet synchronous motor due to their particles falling into local optimization in the late iteration,a novel approach based on chaotic Sine mapping and Gaussian perturbation was introduced to achieve precise parameter recognition for permanent magnet synchronous motors.A nonlinear random decreasing inertia weight was constructed using chaotic Sine mapping,and a Gaussian perturbation strategy was introduced in the"individual cognition"part of the particle swarm.The Sine function was used to construct the learning factor.The improved algorithm only needed to collect the stator current,voltage and speed signals of the motor to achieve the accurate identification of multiple parameters of the permanent magnet synchronous motor.The comparative simulation results show that the improved particle swarm algorithm based on chaos mapping and Gaussian perturbation has faster convergence speed and higher recognition accuracy,which is of great significance for the improvement of permanent magnet synchronous motor control performance.
作者
高森
王康
姜宏昌
胡继胜
GAO Sen;WANG Kang;JIANG Hongchang;HU Jisheng(College of Automation and Electrical Engineering,Dalian Jiaotong University,Dalian 116028,China)
出处
《微特电机》
2023年第11期65-70,共6页
Small & Special Electrical Machines
关键词
永磁同步电机
参数辨识
改进粒子群算法
混沌映射
高斯扰动
permanent magnet synchronous motor(PMSM)
parameter identification
improved particle swarm optimization(PSO)algorithm
chaotic mapping
Gaussian perturbation
