基于EKF算法的交流永磁无刷同步电机参数辨识  

Parameters Identification of Brushless PMSM Based on EKF

作　　者：孙频东[1] 

机构地区：[1]南京师范大学电气与自动化工程学院,江苏南京210042

出　　处：《电气自动化》2008年第3期17-20,共4页Electrical Automation

摘　　要：用扩展的卡尔曼滤波算法(EKF)对表贴式交流永磁无刷同步电动机进行动态参数估计,根据电机控制系统中传感器检测到的电机定子电流、电压和转子的位置、转速等信号,推算电机定子绕组的电阻和转子的主磁通值,经过对电机模型和卡尔曼算法的分析,给出了电机控制系统的全阶卡尔曼滤波模型和经过简化处理后的降阶估计模型,用降阶的简化模型对系统进行估计后,得到了定子电阻和转子主磁通的结果,并分析了定子电阻和转子磁通估计值和实际值之间的误差,以及估计值随电机转速和负载大小变化的曲线。电机用脉宽调制(PWM)的电压源逆变器供电,用 TMS320F2812系列数字信号处理器(DsP)作为控制芯片,用矢量控制的策略对电机进行转速控制。The linearized and reduced order Extended Kalman Filter （EKF） is employed in parameter identification including estimate the stator winding resister and rotor flux linkage in surface-mounted permanent magnet brushless AC motor, according to stator voltages, currents, rotor position and speed measured from the motor. After analizing the model of the motor and algorithm of Kalman Filter, a full order and a reduced order Kalman Filter model was established, and the estimated value is also obained through the model. The error between real value and estimated value was showed in the figure and the curves of the resistance and flux^hnkage, which varies with speed and load are also shown in the figures. In the motor control system, PWM inverter is used as power supply of the motor, and micro DSP TMS320F281 is employed as controller. At the end of the paper, the estimated results of stator resistor and rotor flux linkage are presented.

关 键 词：扩展的卡尔曼算法 交流永磁电机 参数辨识 降阶模型 

分 类 号：TM921.47[电气工程—电力电子与电力传动]