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

Bo Xu, Fangqiang Mu, Guoding Shi, Wei Ji and Huangqiu Zhu
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Bo Xu: The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Fangqiang Mu: The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Guoding Shi: The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Wei Ji: Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry, Jiangsu University, Zhenjiang 212013, China
Huangqiu Zhu: The School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2016, vol. 9, issue 7, 1-14

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.

Keywords: permanent magnet synchronous motor; square root unscented Kalman filter; state estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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