Identification of PMSM based on EKF and elman neural network

• Published in: 2009 IEEE International Conference on Automation and Logistics pp. 1459 - 1463
• Main Authors: Wang Song, Shi Shuang-shuang, Chen Chao, Yang Gang, Qu Zhi-jian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2009
• Subjects: EKF; Elman NN; Equations; Filters; Machine vector control; Mathematical model; Neural networks; Nonlinear systems; Parameter estimation; parameter identification; Permanent magnet motors; PMSM; Stators; Synchronous motors
• ISBN: 9781424447947; 1424447941
• ISSN: 2161-8151
• DOI: 10.1109/ICAL.2009.5262728

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 R s , Psi d and Psi q 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.