MIMO modeling of switched reluctance motors based on LSSVM

被引：0

作者：

Xu, Yu-Zhe ^{[1
,2
,3
]}

Cao, Yan-Ping ^{[3
]}

Zhong, Rui ^{[3
]}

机构：

[1] Shandong Provincial Key Laboratory of Ocean Environment Monitoring Technology, Qingdao

[2] Institute of Oceanographic Instrumentation, Shandong Academy of Science, Qingdao

[3] National ASIC System Engineering Research Center, Southeast University, Nanjing

来源：

Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2015年 / 19卷 / 06期

关键词：

Least squares support vector machine; MIMO; Nonlinear model; Switched reluctance motors;

D O I：

10.15938/j.emc.2015.06.007

中图分类号：

学科分类号：

摘要：

A nonlinear multi-input multi-output (MIMO) model based on least squares support vector machine (LSSVM) was built according to the measured prototype characteristics, aiming at the problem of accumulated errors with multi-variable output of nonlinear switched reluctance motor (SRM) model. An improvement in structure was conducted compared with the traditional LSSVM modeling of SRM, and the inputs of LSSVM are angle and flux, and, outputs are current, torque and flux partial derivatives of the angle, what's more, this multi-output LSSVM avoids the accumulated errors caused by the previous combination of multi single-output LSSVM. The simulation of LSSVM model was complemented in Simulink using S function. It has been shown by experiments that the current waveform obtained by simulation is consistent with the experimental current waveform. When the peak current is 42A, peak angle is 6.36° and turn-off current is 29A in experiment, the corresponding simulation results are 43.5A, 6.58° and 29A, which verify the correctness and effectiveness of the proposed modeling method. ©, 2015, Editorial Department of Electric Machines and Control. All right reserved.

引用

页码：41 / 46

页数：5

共 16 条

[1]

Soares F., Branco P.J.C., Simulation of a 6/4 switched reluctance motor based on Matlab/Simulink environment, IEEE Transactions on Aerospace and Electronic, 37, 3, pp. 989-1009, (2001)

[2]

Spong M., Peresada S., Tayler D., Feedback linearizing control of switched reluctance motors, IEEE Transactions on Automatic Control, 32, 5, pp. 371-379, (1987)

[3]

Krishnan R., Switched Reluctance Motor Drives: Modeling, Simulation, Analysis, Design, and Applications, pp. 1-25, (2001)

[4]

Elmas C., Sagiroglu S., Colak I., Et al., Modeling of a nonlinear switched reluctance deriver based on artificial neural networks, Proceedings of Fifth International Conference on Power Electronics and Variable-Speed Drivers, pp. 7-12, (1994)

[5]

Cai J., Deng Z., Liu Z., Nonlinear Modeling of Switched Reluctance Motor Using Different Methods, Applied Power Electronics Conference and Exposition (APEC), 2010 Twenty-Fifth Annual IEEE, pp. 1018-1025, (2010)

[6]

Kuai S., Zhang X., Wang Q., Et al., Position sensorless control of SRM using neural network, Electric Machines and Control, 15, 8, pp. 18-22, (2011)

[7]

He Z., Xia C., Zhou Y., Et al., Rotor Position Estimation for Switched Reluctance Motor Using Support Vector Machine, Control and Automation, 2007, ICCA 2007, pp. 1683-1687, (2007)

[8]

Zhou J., Li H., Sun Y., Variable Speed Control in Switched Reluctance Drive Based on Least Square-Support Vector Machine, Mechatronics and Automation, 2008, ICMA 2008, pp. 792-796, (2008)

[9]

Qu B., Song J., Jia H., Et al., Nonlinear Modeling of Switched Reluctance Machine, Electrical Machines and Systems (ICEMS 2011), pp. 1-4, (2011)

[10]

Song J., Zheng J., Qu B., Et al., Modeling and simulation for switched reluctance motor based on least squares support vector machine, Electric Machine and Control, 14, 5, pp. 32-36, (2010)

← 1 2 →