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THE RESEARCH ON FLUX LINKAGE CHARACTERISTIC BASED ON BP AND RBF NEURAL NETWORK FOR SWITCHED RELUCTANCE MOTOR

By Y. Cai, S. Sun, C. Wang, and C. Gao

Full Article PDF (444 KB)

Abstract:
The flux and torque of switched reluctance motor (SRM) have a highly nonlinear functional relationship with rotor position and phase current, as a consequence of the double-salient structure of the stator and rotor pole and highly magnetic saturation, which is difficult to build an accurate analytic model. In order to achieve the SRM high-performance control, it is necessary to build an accurate nonlinear model for SRM. On the basis of the adequate and precise sample data, by taking advantage of neural network that has outstanding nonlinear mapping capability, this paper adopts the Back Propagation (BP) based on Levenberg-Marquardt (LM) algorithm and Radial Basis Function (RBF) neural networkto build offline models for SRM respectively. Under different requirements of model accuracy, two kinds of network are studied and compared with each other on accuracy, scale and other aspects. The research results indicate that the network scale built as SRM nonlinear model by BP neural network based on LM algorithm is smaller than the one built by RBF. Additionally, the model accuracy is higher. In terms of the Switched Reluctance Drive (SRD) which requires real-time controller, reducing the network scale will be beneficial to the online real-time control of the system.

Citation:
Y. Cai, S. Sun, C. Wang, and C. Gao, "The Research on Flux Linkage Characteristic Based on BP and Rbf Neural Network for Switched Reluctance Motor," Progress In Electromagnetics Research M, Vol. 35, 151-161, 2014.
doi:10.2528/PIERM14011604

References:
1. Stephenson, J. M. and J. Corda, "Computation of torque and current in doubly salient reluctance motors from nonlinear magnetization data," IEE Proceedings, Vol. 126, No. 5, 393-396, 1979.

2. Chi, H. P., R. L. Lin, and J. F. Chen, "Simplified flux linkage model for switched reluctance motors," IEE Proceedings of Electrical Power Application, Vol. 152, No. 3, 577-583, 2005.
doi:10.1049/ip-epa:20045207

3. Roux, C. and M. M. Morcos, "A simple model for switched reluctance motors," IEEE Power Engineering Review, Vol. 20, No. 10, 49-52, 2000.
doi:10.1109/39.876885

4. Xue, X. D., K. W. E. Cheng, S. L. Ho, and K. F. Kwok, "Trigonometry-based numerical method to compute nonlinear magnetic characteristics in switched reluctance motor," IEEE Transactions on Magnetics, Vol. 43, No. 4, 1845-1848, 2007.
doi:10.1109/TMAG.2007.892619

5. Ilic'-Spong, M., R. Marino, S. M. Peresada, and D. Taylor, "Feedback linearizing control of switched reluctance motors," IEEE Transactions on Automation Control, Vol. 32, No. 5, 371-379, 1987.
doi:10.1109/TAC.1987.1104616

6. Xu, L. and E. Ruchkstater, "Direct modeling of switched reluctance machine by coupled field-circuit method," IEEE Transactions Energy Conversion, Vol. 10, No. 3, 446-454, 1995.
doi:10.1109/60.464867

7. Sun, Y., J. Wu, and Q. Xiang, "The mathematic model of bearingless switched reluctance motor based on the finite-element analysis," Proceedings of the CSEE, Vol. 27, No. 12, 33-40, 2007.

8. Sahoo, N. C., S. K. Panda, and P. K. Dash, "A fuzzy logic based current modulator for torque ripple minimization in switched reluctance motors," Electric Machines and Power Systems, Vol. 27, No. 2, 181-194, 1999.
doi:10.1080/073135699269389

9. Elmas, C., S. Sagiroglu, I. Colak, and G. Bal, "Modeling of a nonlinear switched reluctance drive based on artificial neural networks," Power Electronics and Variable-Speed Drives, 7-12, 1994.

10. Cai, J., Z. Q. Deng, R. Y. Qi, Z. Y. Liu, and Y. H. Cai, "A novel BVC-RBF neural network based system simulation model for switched reluctance motor," IEEE Transactions on Magnetics, Vol. 47, No. 4, 830-838, 2011.
doi:10.1109/TMAG.2011.2105273

11. Xiu, J., C. Xia, and S. Wang, "Modeling of switched reluctance motor based on pi-sigma fuzzy neural network," Transactions of China Electrotechnical Society, Vol. 24, No. 8, 46-64, 2009.

12. Liang, D. and W. Ding, "Modeling and predicting of a switched reluctance motor drive using radial basis function network-based adaptive fuzzy system," IET Electric Power Applications, Vol. 3, No. 3, 218-230, 2009.
doi:10.1049/iet-epa.2008.0096

13. Xu, A., Y. Fan, and Z. Li, "Modeling of switched reluctance motor based on GA-ANFIS," Electric Machines and Control, Vol. 15, No. 7, 54-59, 2011.

14. Si, L., H. Lin, and Z. Liu, "Modeling of switched reluctance motors based on LS-SVM," Proceedings of the CSEE, Vol. 27, No. 6, 26-30, 2007.

15. Shang, W., S. Zhao, and Y. Shen, "Application of LSSVM optimized by genetic algorithm to modeling of switched reluctance motor," Proceedings of the CSEE, Vol. 29, No. 12, 65-69, 2009.

16. Lachman, T., T. R. Mohamad, and C. H. Fong, "Nonlinear modeling of switched reluctance motors using artificial intelligence techniques," IEE Proceedings of Electrical Power Application, Vol. 151, No. 1, 53-60, 2004.
doi:10.1049/ip-epa:20040025

17. Cai, Y., Z. Xu, and C. Gao, "Building of a nonlinear model of switched reluctance motor by BP neural networks," Journal of Tianjin University, Vol. 38, No. 10, 869-873, 2005.


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