基于降阶推广卡尔曼滤波算法的交流感应电动机无速度传感器矢量控制系统

提出了一种估算交流感应电动机转子角速度和转子磁链的降阶推广卡尔曼滤波算法。该算法仅以转子磁链的2个分量作为状态变量,把转子角速度作为被估计的参数,通过定子电压参考值和定子电流测量值疾时估算转子角速度和转子磁链。估算得到的转子磁链用于交流感应电动机无速度传感器矢量控制系统的磁场定向和磁链控制,估算的转子角速度用于速度控制。仿真结果显示,转子角速度和转子磁链的估算精度较高,速度和矢量控制的性能在整个速度范围内令人满意;同时,算法阶数的降低显著地减少了运算量。本文算法能够在实际系统中实时实现。

Speed Sensorless Vector Control of Induction Motor Based on Reduced Order Extended Kalman Filter

A vector control of induction motor by the estimated rotor speed and rotor flux using a new reduced-order extended Kalman filter was proposed. Only two rotor flux components are selected as the state variables and the rotor speed is regarded as a parameter. The reduced-order extended Kalman filter was employed to estimate the rotor speed and rotor flux of an induction motor on line by using the stator voltage references and the measured stator currents. The estimated rotor flux is used for the field orientation and flux control, and the estimated speed is used for the overall speed control. The algorithm order reduction decreases the computational complexity and makes the proposed estimator technologically feasible to be implemented in real time. Computer simulations and experiments of the speed control were carried out to test the usefulness of the estimation algorithm. The results show good performance of the rotor flux and speed estimations.