基于自适应EKF的永磁同步电机无传感控制

Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive EKF Algorithm

传统扩展卡尔曼滤波(EKF)算法会受到永磁同步电机在实际运行中电机参数变化的影响,在速度的估算过程中会产生较大抖动,造成位置估算结果偏差。通过引入类Sigmoid函数来构建自适应EKF算法,用类Sigmoid函数取代传统EKF算法中关键的固定参数,实现参数的动态调整,抑制电机参数变化带来的扰动,降低超调量。建立仿真模型进行仿真验证,结果表明,自适应EKF算法相比于传统最优参数EKF算法,动态响应速度更快,抗干扰能力和鲁棒性更强,转速误差缩小了约60%,转子位置估算误差缩小了约9%。

The traditional extended Kalman filter(EKF)algorithm is affected by the changes of motor parameters in the actual operation of the permanent magnet synchronous motor,which generates large jitter during the estimation of the velocity and causes deviations in the position estimation results.The adaptive EKF algorithm was constructed by introducing the Sigmoid-like function to replace the key fixed parameters in the traditional EKF algorithm with the Sigmoid-like function to realize the dynamic adjustment of parameters,suppress the perturbation caused by the change of motor parameters,and reduce the amount of overshoot.A simulation model for experimental verification was established.The experimental results show that compared with the traditional optimal parameter EKF algorithm,the adaptive EKF algorithm has faster dynamic response speed,stronger anti-interference ability and robustness,the speed error is reduced by about 60%,and the rotor position estimation error is reduced by about 9%.