Research on Model Identification of Permanent Magnet DC Brushless Motor Based on Auxiliary Variable Subspace Identification Algorithm
This paper proposes a model identification method based on the auxiliary variable closed-loop subspace identification algorithm to address the problem of modeling difficulties caused by various complex factors affecting permanent magnet brushless DC motors in practical working conditions. This metho...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/16/6/297 |
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| Summary: | This paper proposes a model identification method based on the auxiliary variable closed-loop subspace identification algorithm to address the problem of modeling difficulties caused by various complex factors affecting permanent magnet brushless DC motors in practical working conditions. This method breaks through the limitations caused by the correlation between input signals and noise in traditional subspace identification algorithms. By introducing auxiliary variables, it effectively avoids the projection process, simplifies the complex calculations of principal component analysis, and improves the practicality and efficiency of the algorithm. When constructing a data-driven identification model, the actual situation of measurement data being contaminated by noise has to be fully considered. Orthogonal compensation matrices and auxiliary variables were used to construct uncorrelated terms for noise, thereby eliminating the negative impact of noise on the model’s identification accuracy. The effectiveness of the proposed identification algorithm was verified by collecting data through a chassis dynamometer simulation test of a vehicle-mounted permanent magnet brushless DC motor. The results show that compared with the traditional N4SID algorithm, the proposed closed-loop subspace identification algorithm based on auxiliary variable principal component analysis exhibits higher model identification accuracy, stronger anti-interference ability, and better stability in both noise-free and noise-contaminated conditions, providing a more reliable model basis for motor performance evaluation and control strategy design. |
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| ISSN: | 2032-6653 |