TY - GEN
T1 - Comparison of state-of-the-art estimators for electrical parameter identification of PMSM
AU - Li, Xinyue
AU - Kennel, Ralph
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this paper, four state-of-the-art online estimation approaches, i.e. recursive least square (RLS) approach, model reference adaptive system (MRAS), extended Kalman filter (EKF) and unscented Kalman filter (UKF) for parameter identification of permanent magnet synchronous machines (PMSM) are implemented and compared. Moreover, a promising estimation method, the moving horizon estimator (MHE), is also investigated. The performance comparison is conducted with simulations and experiments under various scenarios on a permanent magnet synchronous motor among these five techniques.
AB - In this paper, four state-of-the-art online estimation approaches, i.e. recursive least square (RLS) approach, model reference adaptive system (MRAS), extended Kalman filter (EKF) and unscented Kalman filter (UKF) for parameter identification of permanent magnet synchronous machines (PMSM) are implemented and compared. Moreover, a promising estimation method, the moving horizon estimator (MHE), is also investigated. The performance comparison is conducted with simulations and experiments under various scenarios on a permanent magnet synchronous motor among these five techniques.
UR - http://www.scopus.com/inward/record.url?scp=85069505270&partnerID=8YFLogxK
U2 - 10.1109/PRECEDE.2019.8753197
DO - 10.1109/PRECEDE.2019.8753197
M3 - Conference contribution
AN - SCOPUS:85069505270
T3 - Proceedings - PRECEDE 2019: 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics
BT - Proceedings - PRECEDE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2019
Y2 - 31 May 2019 through 2 June 2019
ER -