基于SVM逆系统的PMSM云模型整定PID控制

doi:10.16182/j.issn1004731x.joss.20-0286

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (8): 1846-1855.doi: 10.16182/j.issn1004731x.joss.20-0286

基于SVM逆系统的PMSM云模型整定PID控制

李晖, 云昊, 岳红丽

大连海事大学船舶电气工程学院,辽宁大连 116026

收稿日期:2020-06-01 修回日期:2020-08-12 发布日期:2021-08-19
作者简介:李晖(1968-),男,博士,教授,研究方向为智能控制,船舶运动控制。E-mail: dmulihui@dlmu.edu.cn
基金资助:
国家自然科学基金(51809028); 中央高校基本科研业务费专项资金(3132019318)

Cloud Model PID Control of PMSM Based on SVM Inverse System

Li Hui, Yun Hao, Yue Hongli

Marine Electrical Engineering College, Dalian Maritime University, Dalian, 116026, China

Received:2020-06-01 Revised:2020-08-12 Published:2021-08-19

摘要/Abstract

摘要： 针对多变量、强非线性、高耦合的永磁同步电机(permanent magnet synchronous motor,PMSM),设计了不依赖精确数学模型的支持向量机(support vector machines,SVM)辨识逆系统,应用多变量非线性控制逆系统理论,对PMSM进行动态解耦控制研究;针对直接逆开环控制鲁棒性差及支持向量机建模误差,设计了基于云模型规则推理的参数自整定PID(Proportional Integral Differential)闭环控制器,提高了系统的鲁棒性。仿真结果表明基于SVM逆系统的云模型整定PID控制策略不依赖被控对象数学模型,系统抗干扰能力较强,在实现定子电流励磁分量和转子转速动态解耦的同时转速能够精确跟踪给定响应,系统具有良好的动、静态特性。

关键词: 永磁同步电机, 逆系统方法, 支持向量机, 解耦控制, 云模型整定PID

Abstract: Aiming at the problem of multivariable, nonlinearity and strong coupling of the permanent magnet synchronous motor(PMSM), a strategy of inverse system identification which is independent of precise mathematical model and parameters based on support vector machines(SVM) is proposed. The dynamic decoupling control of PMSM is researched based on multivariable nonlinear control inverse system theory. To deal with direct inverse control open-loop system with poor robustness and inverse modeling error of SVM, a parameter self-tuning PID(Proportional Integral Differential) closed-loop controller based on cloud model rule inference is designed. The simulation results confirm that the cloud model PID control based on SVM inverse system incorporates the merits of model-free learning, strong anti-interference capability. While realizing the dynamic decoupling of the excitation component of stator current and rotor speed, the system has high-precision speed tracking feature and excellent dynamic and static performance.

Key words: permanent magnet synchronous motor(PMSM), inverse system method, SVM, decoupling control, cloud model PID control

中图分类号:

TP391.9

李晖, 云昊, 岳红丽. 基于SVM逆系统的PMSM云模型整定PID控制[J]. 系统仿真学报, 2021, 33(8): 1846-1855.

Li Hui, Yun Hao, Yue Hongli. Cloud Model PID Control of PMSM Based on SVM Inverse System[J]. Journal of System Simulation, 2021, 33(8): 1846-1855.

参考文献

[1] 潘陶红. 不对称偏心气隙结构的电动机车用永磁同步电机研究[D]. 上海: 上海大学, 2019.
Pan Taohong.A Study of PMSM with Asymmetric Eccentric Air Gap Designed for Electric Motorcycle[D]. Shanghai: Shanghai University, 2019.
[2] 杨维山. 一种改进的逆系统永磁同步电机解耦控制技术[J]. 变频器世界, 2017(9): 64-67, 94.
Yang Weishan.An Improved Decoupling Control Technique for Permanent Magnet Synchronous Motor of Invelse System[J]. The World of Inverters, 2017(9): 64-67, 94.
[3] 吴热冰, 李春文. 一般非线性系统的构造性逆系统方法[J]. 控制理论与应用, 2003, 20(3): 345-350.
Wu Rebing, Li Chunwen.Constructive Inverse System Method for General Nonlinear Systems[J]. Control Theory & Applications, 2003, 20(3): 345-350.
[4] Sun X, Shi Z, Chen L, et al.Internal Model Control for a Bearingless Permanent Magnet Synchronous Motor Based on Inverse System Method[J]. IEEE Transactions on Energy Conversion (S0885-8969), 2016, 31(4): 1539-1548.
[5] Shi R, Li T, Kong L K. The Vector Control of PMSM Based on the Inverse System Theory[J]. Applied Mechanics & Materials (S1660-9336), 2014, 494/495: 1587-1590.
[6] 陈哲, 薛钊, 方海伊, 等. 基于自适应逆控制的永磁同步电机调速系统[J]. 西北工业大学学报, 2019, 37(4): 824-829.
Chen Zhe, Xue Zhao, Fang Haiyi, et al.Permanent Magnet Synchronous Motor Speed Control System Based on Adaptive Inverse Control Method[J]. Journal of Northwestern Polytechnical University, 2019, 37(4): 824-829.
[7] 宫玉琳. 永磁同步电动机自适应逆控制系统设计[J].长春理工大学学报(自然科学版), 2015, 38(4): 61-64.
Gong Yulin.Design and Implementation of Adaptive Inverse Control for Permanent Magnet Synchronous Motor[J]. Journal of Changchun University of Science and Technology: Natural Science ed, 2015, 38(4): 61-64.
[8] Wang Q Q, Xu B G, You L R.BPMSM Control Using a Sliding Mode Controller with Fractional Order of Suspension Forces Based on Differential Geometry[J]. International Journal of Control & Automation (S2005-4297), 2015, 8(6): 187-198.
[9] Wenshao B, Fangzhou H, Ziyuan L, et al.Neural Network Inverse System Decoupling Control Strategy of BLIM Considering Stator Current Dynamics[J]. Transactions of the Institute of Measurement & Control (S0142-3312), 2018, 41(11): 014233121876299.
[10] Vapnik V N.The Nature of Statistical Learning Theory[M]. New York: Springer, 2000.
[11] 刘国海, 董蓓蓓, 滕成龙, 等. 基于支持向量机广义逆的永磁同步电机模型参考自适应控制[J]. 东南大学学报(自然科学版), 2010, 40(增1): 13-18.
Liu Guohai, Dong Beibei, Teng Chenglong, et al.Model Reference Adaptive Control of PMSM based on Support Vector Machines Generalized Inverse[J]. Journal of Southeast University: Natural Science ed, 2010, 40(S1): 13-18.
[12] 李德毅, 孟海军, 史雪梅. 隶属云和隶属云发生器[J]. 计算机研究与发展, 1995, 32(6): 15-20.
Li Deyi, Meng Haijun, Shi Xuemei.Membership Cloud and Membership Cloud Generators[J]. Journal of Computer Research and Development, 1995, 32(6): 15-20.
[13] 曲志明. 船舶柴油发电机建模与转速云模型控制研究[D]. 大连: 大连海事大学, 2017.
Qu Zhiming.Research on Modeling and Cloud Model Speed Control of Marine Diesel-engine Generator[D]. Dalian: Dalian Maritime University, 2017.

[1]	位晶晶, 刘勤明, 叶春明, 李冠林. 基于GA-SVR的小样本数据缺失下的设备故障诊断[J]. 系统仿真学报, 2021, 33(6): 1342-1349.
[2]	余伟, 梁恒辉, 罗映. 一种改进差分进化算法的分数阶系统辨识研究[J]. 系统仿真学报, 2021, 33(5): 1157-1166.
[3]	邵诚, 孙宏远, 张林. 基于PSO-LSSVM的反舰导弹自控弹道仿真[J]. 系统仿真学报, 2021, 33(4): 918-926.
[4]	马立新, 朱勇杰, 季乐延. 神经网络优化的无感永磁同步电机控制系统[J]. 系统仿真学报, 2021, 33(3): 622-630.
[5]	刘薛勤, 刘宁, 苏中, 王靖骁, 袁超杰. 基于足底压力感知的智能步态识别方法研究[J]. 系统仿真学报, 2021, 33(11): 2572-2578.
[6]	安玉, 焦朋朋, 白紫秀. 考虑多因素的驾驶行为安全评价与风险等级预测[J]. 系统仿真学报, 2021, 33(1): 118-126.
[7]	修素朴, 文元桥, 肖长诗, 元海文, 詹文强. 倾转四旋翼自主降落的设计与仿真[J]. 系统仿真学报, 2020, 32(9): 1676-1685.
[8]	於万里, 王艳, 纪志成. 氨糖发酵过程建模与工艺参数优化研究[J]. 系统仿真学报, 2020, 32(10): 1895-1902.
[9]	王琳, 仲崇权. 交流伺服系统网络控制的仿真研究[J]. 系统仿真学报, 2019, 31(8): 1674-1682.
[10]	侯利民, 任一夫, 刘恒飞, 林栋. 基于无速度传感器的永磁同步电机滑模自抗扰控制[J]. 系统仿真学报, 2019, 31(5): 963-970.
[11]	尹星, 袁登科, 甄永成, 邵仲书, 吕相杰. 基于SVPWM的永磁同步电机DTC技术[J]. 系统仿真学报, 2019, 31(11): 2535-2542.
[12]	张青, 邹湘军, 林桂潮, 孙艳辉. 草莓重量和形状图像特征提取与在线分级方法[J]. 系统仿真学报, 2019, 31(1): 7-9.
[13]	任志玲, 张钟保, 侯利民, 张广全, 林冬, 赵星. 基于IMC观测器的永磁同步电机预测控制[J]. 系统仿真学报, 2019, 31(1): 94-101.
[14]	陈莉, 张海侠. 基于熵权-云模型的我国绿色智慧城市评价[J]. 系统仿真学报, 2019, 31(1): 136-144.
[15]	沈艳霞, 靳保龙. 永磁同步电机模糊遗忘因子最小二乘法参数辨识[J]. 系统仿真学报, 2018, 30(9): 3404-3410.

基于SVM逆系统的PMSM云模型整定PID控制

Cloud Model PID Control of PMSM Based on SVM Inverse System

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价