Gaussian process-based parameter identification and model current predictive control strategy of PMSM

WEI Zongen; DENG Yongting; QIAO Tingting; FEI Qiang; LI Hongwen

doi:10.37188/OPE.20233104.0479

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Gaussian process-based parameter identification and model current predictive control strategy of PMSM

Micro\/Nano Technology and Fine Mechanics | 更新时间：2026-09-12

- Gaussian process-based parameter identification and model current predictive control strategy of PMSM
- Optics and Precision Engineering Vol. 31, Issue 4, Pages: 479-490(2023)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所，吉林长春 130033
  2.中国科学院大学，北京 100039
  3.陆军装甲兵学院士官学校，吉林长春 130000
  4.季华实验室，广东佛山 528200
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233104.0479
  CLC： TM351;TP273
- Received：30 August 2020，
  
  Revised：2020-09-23，
  
  Published：25 February 2023
- 稿件说明：
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魏宗恩,邓永停,乔延婷等.基于高斯过程的参数辨识及永磁同步电机模型电流预测控制策略[J].光学精密工程,2023,31(04):479-490.

WEI Zongen,DENG Yongting,QIAO Tingting,et al.Gaussian process-based parameter identification and model current predictive control strategy of PMSM[J].Optics and Precision Engineering,2023,31(04):479-490.
魏宗恩,邓永停,乔延婷等.基于高斯过程的参数辨识及永磁同步电机模型电流预测控制策略[J].光学精密工程,2023,31(04):479-490. DOI： 10.37188/OPE.20233104.0479.

WEI Zongen,DENG Yongting,QIAO Tingting,et al.Gaussian process-based parameter identification and model current predictive control strategy of PMSM[J].Optics and Precision Engineering,2023,31(04):479-490. DOI： 10.37188/OPE.20233104.0479.

摘要

为了提高永磁同步电机控制系统电流环控制器的性能，降低模型参数失配对控制系统的影响，提出了基于高斯过程参数辨识的永磁同步电机有限集模型预测电流控制策略（FCS-GPMPC）。首先，介绍了永磁同步电机电流预测模型并分析了模型参数失配对系统性能的影响；其次，为简化一般机器学习参数辨识算法中超参数复杂的调试过程，提出了一种基于高斯过程的模型参数的辨识方法；同时，引入预测值的置信区间作为参数预测效果的实时评估参考；最后，将高斯过程参数辨识与基于模型的有限集模型预测电流控制（FCS-MPC）相结合，在得到准确辨识的参数后对系统电流预测模型更新以提高系统鲁棒性和电流环跟踪性能。实验结果显示：在本文训练数据的统计特征下，测试数据均方根误差RMSE为0.002 1，

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3.38666677

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达到0.99。在参数波动条件下，与FCS-MPC相比，FCS-GPMPC策略下电流波动度降低了30.5%，电流平均偏移度降低了19.6%，另外对参考电流的阶跃变化，FCS-GPMPC有更好的动态响应。实验结果表明，基于高斯过程的模型预测控制方法可有效抑制模型失配对控制系统的影响，能够提高永磁同步电机控制系统电流控制器性能。

Abstract

This paper proposes a model predictive control （MPC） method for permanent magnet synchronous motors （PMSMs） based on finite control set Gaussian process MPC （FCS-GPMPC） parameter identification to limit the influence of model mismatches on the control system and to improve the current controller performance of control systems in a PMSM. First， the current PMSM prediction model is introduced and the influence of model parameter mismatches on the system performance is analyzed. Secondly， in order to simplify the complex debugging process of hyperparameters in general machine learning parameter identification algorithms， the GPMPC method is proposed. At the same time， the confidence interval of the predicted value is introduced as a real-time evaluation reference for the parameter prediction effect. Finally， the GP parameter identification method is combined with the FCS-MPC to predict the system current after accurately obtaining the identified parameters. The model is updated to improve system robustness and current loop tracking performance. The experimental results show that under the statistical characteristics of the training data， the root mean square error and of the test data are 0.0021 and 0.99， respectively. Under the condition of parameter fluctuation， compared with FCS-MPC， FCS-GPMPC reduces current fluctuation by 30.5% and the average current offset by 19.6%. In addition， for step changes in the reference current， FCS-GPMPC has a better dynamic response. The proposed GP-MPC can effectively suppress the influence of model mismatch on control systems and can improve the performance of the current controller of PMSM control systems.

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references

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