期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 68, 期 9, 页码 8540-8549出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2020.3018078
关键词
Torque; Coils; Rotors; Permanent magnet motors; Gaussian processes; Permanent magnets; Induction motors; Multitask Gaussian process (MTGP); permanent magnet spherical motor (PMSM); torque calculation
资金
- National Natural Science Foundation of China [51637001]
- Natural Science Research Key Program of Anhui Provincial Education Department [KJ2017A001]
- Young Core Teacher Program of Anhui University [J01005126]
This article applies Gaussian process to establish the relationship between rotor position and output torque for PMSMs, solving complex three-dimensional problems using a data-driven approach. It also develops multitask Gaussian process to calculate the total torque produced by multiple coils, exhibiting superior performance in accuracy.
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.
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