State Estimation of Bearingless Permanent Magnet Synchronous Motor Using Improved UKF
Unscented Kalman filter (UKF) algorithm was widely used in the speed sensorless control of Motor. However, the problem of bad robustness of the model parameter change, slow convergence and lower tracking ability to abrupt state still exist. Combined with strong tracking filter, an improved UKF is proposed in this paper. The time-varying fading factor and softening factor are introduced to adaptively adjust gain matrices and the state forecast covariance square root matrix, in order to realize the residuals sequences orthogonality and force the UKF to track the real state rapidly. The speed sensorless vector control system of bearingless permanent magnet synchronous motor (BPMSM) was set up based on this estimation approach. The simulation results illustrate that, contrast to ordinary UKF, the proposed method is capable of precisely estimating the rotor speed and space position, high robustness is achieved under the conditions of step response or load disturbance.
Unscented Kalman filter (UKF) Strong tracking filter Bearingless permanent magnet synchronous motor (BPMSM) State estimation
XU Bo ZHU Huangqiu JI Wei
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
4430-4433
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）