An EKF for PMSM sensorless control based on noise model identification using Ant Colony Algorithm

Author

Wang, Anbang ; Wang, Qunjing ; Hu, Cungang ; Qian, Zhe ; Ju, Lufeng ; Liu, Jun

Author_Institution

Sch. of Electr. & Autom. Eng., Hefei Univ. of Technol., Hefei, China

fYear

2009

fDate

15-18 Nov. 2009

Firstpage

1

Lastpage

4

Abstract

It is a hot topic that an extended Kalman filter (EKF) is used as speed and position observer in sensorless control of permanent magnet synchronous motor (PMSM). However, the choice of the EKF covariance matrices is still an unsolved problem as the EKF is applied in sensorless drives. In this paper, the parameters of the covariance matrices are tuned based on ant colony algorithm (ACA). The simulation results show the validity of the procedure.

Keywords

Kalman filters; covariance matrices; permanent magnet motors; sensorless machine control; synchronous motors; EKF; PMSM sensorless control; ant colony algorithm; covariance matrices; extended Kalman filter; noise model identification; permanent magnet synchronous motor; Ant colony optimization; Covariance matrix; Gaussian noise; Noise measurement; Nonlinear equations; Permanent magnet motors; Sensorless control; State estimation; Stators; Voltage; Ant Colony Algorithm; Permanent Magnet Synchronous Motor; extended Kalman filter; sensorless control;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Machines and Systems, 2009. ICEMS 2009. International Conference on

Conference_Location

Tokyo

Print_ISBN

978-1-4244-5177-7

Electronic_ISBN

978-4-88686-067-5

Type

conf

DOI

10.1109/ICEMS.2009.5382871

Filename

5382871