[Poster Presentation]Parameter Identification of Permanent Magnet Synchronous Linear Motors Using Multi-Innovation Least Squares Method

IMPORTANT DATES

Deadline for digest submission: Jan, 31, 2021
Notification of acceptance: Mar, 10, 2021
Full paper submission: May, 31, 2021

Parameter Identification of Permanent Magnet Synchronous Linear Motors Using Multi-Innovation Least Squares Method

ID：414 View Protection：PUBLIC Updated Time：2021-06-27 20:16:09 Hits：1014 Poster Presentation

Start Time：Pending （Asia/Shanghai）

Duration：Pending

Session：[No Session] » [No Session Block]

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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 University

Hongda 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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