神经网络优化的无感永磁同步电机控制系统

Neural Network Optimized Sensorless Permanent Magnet Synchronous Motor Control System

针对永磁同步电机(Permanent Magnet Synchronous Motor,PMSM)转速和转子位置容易受到传感器传输信号精度不佳的问题,提出了扩展卡尔曼滤波算法来测算电机转速和转子位置的无传感控制系统,采用BP (Back-ProPagation)神经网络算法优化EKF (Extended Kalman Filter)算法的协方差矩阵,提高了转速、转子位置测算值的精确度。同时采用速度滑模控制器结合电流前馈解耦单元,改善整个控制系统的稳定性。仿真结果表明了该套系统可以对转速、转子位置进行精确测算,转子位置偏差值在±0.3 rad左右波动,与传统PI控制相比,转速恢复时间缩短了50%,超调极小,具有更强的鲁棒性,在电机控制中有较强的实际应用价值。

In order to solve the poor accuracy of the speed and rotor position of permanent magnet synchronous motor caused by sensor,a sensorless control system is proposed to calculate the speed and rotor position of PMSM with extended Kalman filtering algorithm.BP neural network algorithm is used to optimize the covariance matrix Q and R of EKF,which improves the accurate calculation values of rotational speed and rotor position.At the same time,the speed sliding mode controller combined with the current feed-forward decoupling unit are used to improve the stability of the whole control system.The simulation results show that the system can accurately calculate speed and rotor position and the deviation value of rotor position fluctuates around±0.3 rad.Compared with the traditional PI control,the speed recovery time is shortened by 50%,and the overshoot is very small,the robustness is stronger.It has strong practical application value in motor control.