无传感器PMSM中基于IGSO优化EKF的速度估计方法  

Method of EKF speed estimation for sensorless PMSM based on IGSO optimizing

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作  者:张相胜 田佳文 潘丰 Zhang Xiangsheng;Tian Jiawen;Pan Feng(Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education,Jiangnan University,Wuxi Jiangsu 214122,China)

机构地区:[1]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122

出  处:《计算机应用研究》2019年第4期1006-1009,1014共5页Application Research of Computers

摘  要:为了提高无传感器永磁同步电机(PMSM)控制系统中速度控制性能,提出一种基于改进群搜索优化(IGSO)算法的扩展卡尔曼滤波(EKF)速度估计方案。首先,分析了PMSM磁场定向控制(FOC)系统模型;然后,将电机的d-q轴电压、电流和转子速度作为状态变量,构建EKF中的状态方程来估计转速和负载。同时,为了提高EKF的估计性能,以估计值与实际值的平方误差积分(ISE)作为适应度函数,通过IGSO算法来优化EKF中的噪声协方差矩阵Q和R,以此获得最优参数。仿真结果表明,提出的控制系统能够精确地估计出电机转速并进行有效控制。In order to improve the speed control performance in sensorless permanent magnet synchronous motor(PMSM)control system,this paper proposed an extended Kalman filter(EKF)speed estimation scheme based on improved group search optimization(IGSO)algorithm.Firstly,it analyzed the PMSM field-oriented control(FOC)system model.Then,it used the motor's d-q axis voltage,current and rotor speed as state variables to construct the state equation,so as to use the EKF to estimate the speed and load.At the same time,in order to improve the estimated performance of EKF,it estimated the square error integral(ISE)between the value and the actual value as the fitness function,and used the IGSO algorithm to optimize the noise cova-riance matrix Q and R in EKF.The simulation results show that the proposed control system can accurately estimate the motor speed and control effectively.

关 键 词:永磁同步电机 速度估计 扩展卡尔曼滤波 噪声协方差矩阵 群搜索优化 

分 类 号:TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]

 

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