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

doi:10.37188/OPE.20233104.0479

CIOMP OpenIR

	Gaussian process-based parameter identification and model current predictive control strategy of PMSM
	Z. Wei, Y. Deng, T. Qiao, Q. Fei and H. Li
	2023
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	1004924X
卷号	31 期号:4 页码:479-490
摘要	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，re-spectively. 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. © 2023 Chinese Academy of Sciences. All rights reserved.
DOI	10.37188/OPE.20233104.0479
URL	查看原文
收录类别	ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/68012
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Z. Wei, Y. Deng, T. Qiao, Q. Fei and H. Li. Gaussian process-based parameter identification and model current predictive control strategy of PMSM[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2023,31(4):479-490.
APA	Z. Wei, Y. Deng, T. Qiao, Q. Fei and H. Li.(2023).Gaussian process-based parameter identification and model current predictive control strategy of PMSM.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,31(4),479-490.
MLA	Z. Wei, Y. Deng, T. Qiao, Q. Fei and H. Li."Gaussian process-based parameter identification and model current predictive control strategy of PMSM".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 31.4(2023):479-490.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Gaussian process-bas（2329KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载