Speed-sensorless control system of a bearingless induction motor based on iterative central difference Kalman filter

In order to obtain speed self-detecting with low cost for a bearingless induction motor (BIM) a speed-sensorless control strategy based on the iterative central difference Kalman filter (ICDKF) is proposed. Firstly, on the basis of the BIM mathematical model, the nonlinear state equation is established and its order is reduced from fifth-order to fourth-order using the stator terminal voltage and current as input. Then, a sterling interpolation formulation is used in the filter to reduce the model error, and an iteration loop link is adopted to improve the filter accuracy. Finally, the online speed of the BIM is identified through the filter rotor speed estimation. Theoretical analysis, simulation and experimental results by UKF and CDKF method have been compared. The results show that the proposed speed-sensorless control system not only has good speed tracking performance and reduce the load disturbance but also improves the BIM suspension performance.