State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF
AbstractThis paper focuses on an improved square root unscented Kalman filter (SRUKF) and its application for rotor speed and position estimation of permanent magnet synchronous motor (PMSM). The approach, which combines the SRUKF and strong tracking filter, uses the minimal skew simplex transformation to reduce the number of the sigma points, and utilizes the square root filtering to reduce computational errors. The time-varying fading factor and softening factor are introduced to self-adjust the gain matrices and the state forecast covariance square root matrix, which can realize the residuals orthogonality and force the SRUKF to track the real state rapidly. The theoretical analysis of the improved SRUKF and implementation details for PMSM state estimation are examined. The simulation results show that the improved SRUKF has higher nonlinear approximation accuracy, stronger numerical stability and computational efficiency, and it is an effective and powerful tool for PMSM state estimation under the conditions of step response or load disturbance. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Xu, B.; Mu, F.; Shi, G.; Ji, W.; Zhu, H. State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF. Energies 2016, 9, 489.
Xu B, Mu F, Shi G, Ji W, Zhu H. State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF. Energies. 2016; 9(7):489.Chicago/Turabian Style
Xu, Bo; Mu, Fangqiang; Shi, Guoding; Ji, Wei; Zhu, Huangqiu. 2016. "State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF." Energies 9, no. 7: 489.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.