在强跟踪滤波(Strong track filter, STF)算法和延迟扩展Kalman滤波(Schmidt extended Kalman filter, SEKF)算法的基础上, 提出了强跟踪延迟滤波(Strong track Schmidt filter, STSF)算法, 结合感应电机降阶模型建立了电机状态估计算法, 将其应用于感应电机无速度传感器控制系统中, 并与扩展Kalman滤波(Extended Kalman filter, EKF)、SEKF和STF三种算法的状态估计性能作比较. 仿真和实验结果表明, STSF算法在估计精度、跟踪速度、抑止噪声等方面均优于EKF算法, 并且计算复杂度显著降低, 能有效在线估计电机转速和磁链.
Based on strong track filter (STF) and Schmidt extended Kalman filter (SEKF), a strong track Schmidt filter (STSF) is proposed. By using the reduced-order model of induction motor, a state estimation algorithm is established and is applied to speed sensorless control system of induction motor. Comparison has been made between the extended Kalman filter (EKF), SEKF, and STF algorithms in terms of motor state estimation performance. Simulation and experiment results show that STSF is better than EKF on the estimating accuracy, tracking speed, restraining noise, and moreover, its computational complexity is also largely decreased. It is proved that STSF algorithm can carry out the task of motor speed and flux estimations in real time.