Predictive Current Control of Sensorless Linear Permanent Magnet Synchronous Motor

He Wang, Tao Wu (), Youguang Guo (), Gang Lei and Xinmei Wang
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He Wang: School of Automation, China University of Geosciences, Wuhan 430074, China
Tao Wu: School of Automation, China University of Geosciences, Wuhan 430074, China
Youguang Guo: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
Gang Lei: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
Xinmei Wang: School of Automation, China University of Geosciences, Wuhan 430074, China

Energies, 2023, vol. 16, issue 2, 1-14

Abstract: 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.

Keywords: linear permanent magnet synchronous motor (LPMSM); sensorless control; sliding mode; deadbeat predictive current control (DBPC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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