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Energies 2016, 9(7), 489; doi:10.3390/en9070489

State Estimation of Permanent Magnet Synchronous Motor Using Improved Square Root UKF

1
The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
2
Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Academic Editor: K. T. Chau
Received: 9 April 2016 / Revised: 18 May 2016 / Accepted: 20 June 2016 / Published: 24 June 2016
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Abstract

This 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
Keywords: permanent magnet synchronous motor; square root unscented Kalman filter; state estimation permanent magnet synchronous motor; square root unscented Kalman filter; state estimation
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MDPI and ACS Style

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.

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