DocumentCode :
3481858
Title :
Identification of PMSM based on EKF and elman neural network
Author :
Song, Wang ; Shuang-Shuang, Shi ; Chao, Chen ; Yang Gang ; Zhi-Jian, Qu
Author_Institution :
Sch. of Electr. Eng., Beijing Jiaotong Univ., Beijing, China
fYear :
2009
fDate :
5-7 Aug. 2009
Firstpage :
1459
Lastpage :
1463
Abstract :
Permanent magnet synchronous motor (PMSM) is a complex plant to control, due to its high nonlinearity and strong coupling. At the same time, the variations of motor parameters make this problem more serious. So, parameter identification of PMSM seems to be important for the double closed-loop vector control system. To solve this problem, a new method combining Elman neural network(Elman NN) and modified extended kalman filter(EKF) is studied in this paper. The approach of identifying Rs, Psid and Psiq is discussed. Simlation results show that it has lots of advantages such as high precision, fast convergence and excellent generalization ability and it is suitable for variable speed and variable load disturbance, even more complex circumstance.
Keywords :
Kalman filters; closed loop systems; neural nets; nonlinear control systems; parameter estimation; permanent magnet motors; synchronous motors; EKF; Elman neural network; PMSM; double closed-loop vector control system; modified extended Kalman filter; parameter identification; permanent magnet synchronous motor; Equations; Filters; Machine vector control; Mathematical model; Neural networks; Nonlinear systems; Parameter estimation; Permanent magnet motors; Stators; Synchronous motors; EKF; Elman NN; PMSM; parameter identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation and Logistics, 2009. ICAL '09. IEEE International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-4794-7
Electronic_ISBN :
978-1-4244-4795-4
Type :
conf
DOI :
10.1109/ICAL.2009.5262728
Filename :
5262728
Link To Document :
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