Nonparametric Model Prediction Current Control for PMSM Drives
ID:135 View Protection:PUBLIC Updated Time:2023-06-12 15:18:57 Hits:1209 Poster Presentation

Start Time:2023-06-19 10:15(Asia/Shanghai)

Duration:0min

Session:E Poster Session » E4Poster Session 4

Abstract
     To essentially solve the problem that the control performance of model predictive current control (MPCC) depends on the accuracy of model parameters, a model predictive current control of permanent-magnet synchronous motors (PMSM) based on the nonparametric prediction model (NPM-MPCC) is proposed. Firstly, the MPCC method based on conventional prediction model is introduced, and the model errors of the conventional prediction model on the control performance is analyzed. Then, a nonparametric prediction model for PMSM drive is proposed, which includes d-axis current prediction model and q-axis current prediction model. This proposed model doesn’t include motor parameters, and can achieve current prediction using prediction current error, sampling and stored information. Finally, experimental results verity the effectiveness of the proposed method.
Keywords
MPCC;PMSM drive;hybrid model
Speaker
Zheng Liu
North China University of Technology

Jose Rodriguez
Universidad San Sebastian

Xiaoguang Zhang
North China University of Technology

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Important Date
  • Conference Date

    Jun 16

    2023

    to

    Jun 19

    2023

  • Jun 15 2023

    Contribution Submission Deadline

  • Jul 02 2023

    Registration deadline

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Huazhong University of Science and Technology, China
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