Modeling and Analysis of Permanent Magnet Spherical Motors by A Multi-task Gaussian Process Method and Finite Element Method for Output Torque


Permanent magnet spherical motors (PMSMs) operate on the principle of the dc excitation of stator coils and three freedom of motion in the rotor. Each coil generates the torque in a specific direction, collectively they move the rotor to a direction of motion. Modeling and analysis of the output torque are of critical importance for precise position control applications. The control of these motors requires precise output torques by all coils at a specific rotor position, which is difficult to achieve in the three-dimension space. This article is the first to apply the Gaussian process to establish the relationship of the rotor position and the output torque for PMSMs. Traditional methods are difficult to resolve such a complex three-dimensional problem with a reasonable computational accuracy and time. This article utilizes a data-driven method using only input and output data validated by experiments. The multitask Gaussian process is developed to calculate the total torque produced by multiple coils at the full operational range. The training data and test data are obtained by the finite-element method. The effectiveness of the proposed method is validated and compared with existing data-driven approaches. The results exhibit superior performance of accuracy.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Power Electronics, Machines and Power System (PEMPS)
College of Engineering & Physical Sciences
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Uncontrolled Keywords: Multitask Gaussian process (MTGP),permanent magnet spherical motor (PMSM),torque calculation,Control and Systems Engineering,Electrical and Electronic Engineering
Publication ISSN: 1557-9948
Last Modified: 14 May 2024 07:16
Date Deposited: 27 Aug 2020 07:20
Full Text Link:
Related URLs: https://ieeexpl ... cument/9177338/ (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-09
Published Online Date: 2020-08-25
Accepted Date: 2020-08-06
Authors: Wen, Yan
Li, Guoli
Wang, Qunjing
Guo, Xiwen
Cao, Wenping (ORCID Profile 0000-0002-8133-3020)



Version: Accepted Version

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