Presentation Details
Parameter Identification of Permanent Magnet Synchronous Linear Motors Using Multi-Innovation Least Squares Method
ID：414
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Updated Time：2021-06-27 20:16:09
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Poster Presentation
Start Time：Pending（Asia/Shanghai）
Duration：Pending
Session：No Session »
Abstract
The identification of dynamic model parameters of the permanent magnet synchronous linear motors (PMSLMs) is the foundation of high-performance motor control. To address the low accuracy of modelling of PMSLMs, a parameter identification method of the PMSLM is proposed by using multi-innovation least squares (MILS) approach in this paper. The linear transfer function of the PMSLM is first built. Then the MILS approach based on the recursive least squares (RLS) algorithm is developed to identify the parameters of the built model. Finally, the simulation results of MILS and RLS algorithms are shown, and the effectiveness of the proposed method is verified.
Keywords
permanent magnet synchronous linear motors, system identification, multi-innovation recursive least squares method
Speaker
Mou Hongda
Guangdong Key Laboratory of Electromagnetic Control and Intellignet Robots Shenzhen UniversityHongda Mou is a graduate student from Shenzhen University. He is mainly engaged in the research of motor research in the field of chip manufacturing and rail transportation. He mainly studies the control and application of permanent magnet synchronous linear motors, magnetic levitation planar motors, and planar switched reluctance motors.
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Important Date
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Conference Date
Jul 01
2021
toJul 04
2021
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Jul 03 2021
Contribution Submission Deadline
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Nov 03 2021
Registration deadline
Sponsored By
Huazhong University of Science and Technology, China
Supported By
University of Sydney, Australia
Southwest Jiaotong University, China
Southwest Jiaotong University, China
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