LAPSE:2023.6172
Published Article
LAPSE:2023.6172
Predictive Current Control of Sensorless Linear Permanent Magnet Synchronous Motor
February 23, 2023
In the vector control system of a tubular oscillating permanent magnet synchronous linear motor, it is difficult to obtain accurate feedback information from the conventional mechanical sensors under bad and complex working conditions. This paper presents a new predictive current control designed to estimate the speed of the tubular oscillation permanent magnet synchronous linear motor. It implements two control techniques: The first technique is using the sliding-mode observer’s speed observer for speed estimation. The second is to design a deadbeat predictive current control to replace the PI regulator in the conventional current loop; it solves the difficulties of global optimization and PI parameter setting. The simulation and experimental results show that this method gives a good dynamic performance.
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Keywords
deadbeat predictive current control (DBPC), linear permanent magnet synchronous motor (LPMSM), sensorless control, sliding mode
Subject
Suggested Citation
Wang H, Wu T, Guo Y, Lei G, Wang X. Predictive Current Control of Sensorless Linear Permanent Magnet Synchronous Motor. (2023). LAPSE:2023.6172
Author Affiliations
Wang H: School of Automation, China University of Geosciences, Wuhan 430074, China
Wu T: School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Explora [ORCID]
Guo Y: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia [ORCID]
Lei G: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia [ORCID]
Wang X: School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Explora
Wu T: School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Explora [ORCID]
Guo Y: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia [ORCID]
Lei G: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia [ORCID]
Wang X: School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Explora
Journal Name
Energies
Volume
16
Issue
2
First Page
628
Year
2023
Publication Date
2023-01-04
Published Version
ISSN
1996-1073
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Original Submission
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PII: en16020628, Publication Type: Journal Article
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LAPSE:2023.6172
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doi:10.3390/en16020628
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[v1] (Original Submission)
Feb 23, 2023
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Feb 23, 2023
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