NDLI: Stability and dynamic performance improvement of a sensorless IPMSM drive via adaptive estimated-speed-assisted position prediction and current quality evaluation

Content Provider	IEEE Xplore Digital Library
Author	Yue Zhao Wei Qiao Long Wu
Copyright Year	2013
Description	Author affiliation: John Deere Electron. Solutions, Fargo, ND, USA (Long Wu) \|\| Dept. of Electr. Eng., Univ. of Nebraska-Lincoln, Lincoln, NE, USA (Yue Zhao; Wei Qiao)
Abstract	This paper studies stability issues of sensorless torque control of high power interior permanent magnet synchronous machines (IPMSMs) under large load transients, e.g., complete torque reversals with the highest slew rate allowed. To perform a sensorless vector control, a current model-based sliding-mode observer is utilized to estimate the rotor position. The correlation between current regulation quality and position estimation accuracy is theoretically studied and experimentally verified. Based on the correlation, the accuracy of position estimation can be evaluated online indirectly. Moreover, an adaptive estimated-speed-assisted position prediction (ESAPP) scheme is proposed to improve the stability and performance of the sensorless control systems under large load transients. The ESAPP period is adapted according to the position estimation accuracy. Experimental results on a 150 kW sensorless IPMSM drive system are provided to validate the proposed algorithms.
Starting Page	3473
Ending Page	3480
File Size	1375163
Page Count	8
File Format	PDF
ISBN	9781479903368
DOI	10.1109/ECCE.2013.6647158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Current control Estimation error Torque Accuracy System stability Rotors Sensorless control Observers Interior permanent magent synchronous machine (IPMSM) Sliding-mode observer (SMO) Current regulation Position estimation
Content Type	Text
Resource Type	Article

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