摘要
针对永磁同步电机(PMSM)位置与速度传感器易受外部条件和自身精度的影响,以及PMSM无传感器控制等问题,提出了一种基于扩展卡尔曼滤波(EKF)的PMSM非线性预测无传感器控制方法。该方法具有预测性、自适应能力、抗干扰性、易于软件实现等优点。首先,详细分析了PMSM的矢量控制系统数学模型和EKF原理。其次,将EKF算法应用于PMSM的无传感器矢量控制中,即将电机αβ轴电流和电压作为输入变量,经过EKF算法运算,估算出转子转速和转子位置来代替电机的位置与速度传感器。最后,搭建基于MATLAB/Simulink的PMSM无传感器矢量控制系统仿真模型。仿真结果表明,EKF控制方法能准确估算出电机在空载和负载(随机)时的位置和转速,且具有较好的可预测性和系统响应性。在电机突加负载的情况下,也可以快速恢复到稳定状态,具有较强的抗负载性。
Aiming at the position and speed sensors of permanent magnet synchronous motor( PMSM) are susceptible to external conditions and their own accuracy,as well as the PMSM sensorless control,a nonlinear predictive control approach based on extended Kalman filter( EKF) is proposed. The method has the advantages of predictability,adaptability,anti-interference,and ease in software implementation. First,the mathematical model of vector control system of PMSM and principle of EKF are analyzed in detail. Then,EKF algorithm is applied to sensorless vector control of PMSM,i. e.,the motor αβ shaft current and voltage are taken as input variables,and after EKF algorithm calculation,the rotor speed and rotor position are estimated to replace the motor position and speed sensor. Finally,a simulation model based on MATLAB/Simulink is built. Simulation results show that the EKF control method can accurately estimate the motor position and speed under no-load and load conditions with good predictability and system responsiveness. In the case of a sudden load put on the motor,the motor can quickly resume to a stable state,with strong anti-load resistance capability.
引文
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