Soft sensing of magnetic bearing system based on support vector regression and extended Kalman filter

The rotor displacement measurement plays an important role in an active bearing system, however, in practice this measurement might be quite noisy, so that the control performance might be seriously degraded. In this paper, a soft sensing method for magnetic bearing-rotor system based on Support Vector Regression (SVR) and Extended Kalman Filter (EKF) is proposed. In the proposed method, SVR technique is applied to model the acceleration of the rotor, which is regarded as a nonlinear function of rotor displacement, rotor velocity and bearing currents; then this SVR model is used to construct an EKF estimator of rotor displacement. In the proposed method the bearing current is incorporated to the estimation of displacement, so that displacement can be precisely estimated even if very large observation noise is present. A series of experiments are performed and the results verify the validity of the proposed displacement soft sensing method.