Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System

Liu Zhi Jian; Nguyen Le Minh Tri; Nguyen Le Thai; Phan Xuan Le

doi:doi:10.11648/j.ijepe.20150401.16

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Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System

Liu Zhi Jian, Nguyen Le Minh Tri, Nguyen Le Thai, Phan Xuan Le

Published in International Journal of Energy and Power Engineering (Volume 4, Issue 1)

Received: 5 December 2014 Accepted: 17 December 2014 Published: 6 February 2015

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Abstract

Switched reluctance motors (SRM) have a wide range of applications in industries due to the special properties of this motor. However, because of its dynamical nonlinearities, so the problems control of SRM is complex. This paper proposed an adaptive intelligent controller for SRM with the aim to improve the ripple of torque. First, we use a fuzzy logic controller to control switch-off angle, and then proposes a new controller by means of Adaptive Neural Fuzzy Inference (ANFIS). Simulation results are given to show the efficacy of the proposed method.

Published in	International Journal of Energy and Power Engineering (Volume 4, Issue 1)
DOI	10.11648/j.ijepe.20150401.16
Page(s)	39-45
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

SRM, Fuzzy logic, ANFIS

References

[1]	Yasuharu Ohdachi, Yoshihiro Kawase, Yutaka Miura and Yoji Hayashi, “Optimum design of switched reluctance motors using dynamic finite element analysis,” IEEE Trans Magnet, Vol. 33, No. 2, pp.2033–2036, 1997.
[2]	Perez GE, Ortiz PM, Ramirez HS, “Passivity-based control of switched reluctancemotors with nonlinear magnetic circuits,” IEEE Trans Control System Technol, Vol. 12, No. 3, pp.439–448, May 2004.
[3]	McCann R, Islam M, “Application of a sliding-mode observer forposition and speed estimation in switched reluctance motor drives,” IEEE Trans Ind Appl, Vol. 37, No. 1, pp.51–58, 2001.
[4]	Yamai H, Kaneda M, “Optimal switched reluctance motor drive forhydraulic pump unit,” Proc IEEE Int Conf Ind Appl, Italy, Rome, pp.1555–1562, 2000.
[5]	R. Krishnan, Switched Reluctance Motor Drives, Boca Raton, FL:CRC Press, 2001.
[6]	T. S. Chuang, and C. Pollock, “Robust speed control of a switched reluctance vector drive using variable structure approach,” IEEE Trans. on Industrial Electronics, vol. 44, no. 6, pp. 800–808, Dec. 1997.
[7]	Ferhat Daldaban, Nurettin Ustkoyuncu, Kerim Guney, “Phase inductance estimation for switched reluctance motor using adaptive neuro-fuzzy inference system,” Energy Conversion and Management, pp.485–493, 2006.
[8]	Wen Ding, Deliang Liang, “Modeling of a 6/4 Switched Reluctance Motor Using AdaptiveNeural Fuzzy Inference System,” IEEE Transactions on Magnetics, Vol. 44, No. 7, pp.1796-1804, July 2008.
[9]	Hany M. Hasanien, “Speed Control of Switched Reluctance Motor Using an Adaptive Neuro-fuzzy Controller,” Proceedings of the World Congress on Engineering 2013 Vol II, pp. 1093-1096, July 2013.
[10]	Ahmed Tahour, Hamza Abid, “Abdel Ghani Aissaoui, Adaptive Neuro-Fuzzy Controller of Switched Reluctance Motor,” Serbian Journal Of Electrical Engineering Vol. 4, No. 1, pp 23–34, June 2007.
[11]	Jin-Woo Ahn “Torque Control", book edited by Moulay Tahar Lamchich, ISBN 978-953-307-428-3, pp201-252, February, 2011.
[12]	S. Vijayan, S. Paramasivam, R. Arumugam, S. S. Dash, K. J. Poornaselvan, "A Practical approach to the Design and Implementation of Speed Controller for Switched Reluctance Motor Drive using Fuzzy Logic Controller,” Journal of Electrical Engineering, vol.58, No.1, pp. 39-46, 2007.
[13]	Gamal M. Hashem, Hany M. Hasanien, “Speed Control of Switched Reluctance Motor Based on Fuzzy Logic Controller,” roceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo University, Egypt, pp.288-292, 2010.
[14]	J-S-R.Jang, C-T.Sun, E.Mizutani, “Neuro-fuzzy anh soft computing,” Prentice Hall Upper Saddle river, NJ 07458, 1997.
[15]	A. Abraham, "Adaptation of Fuzzy Inference System Using Neural Learning,” Springer-Verlag Berlin Heidelberg, 2005.
[16]	T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE transactions on systems, man, and cybernetics, Vol. SMC-15, No.1, 1985, pp. 116–132, January/February.
[17]	Chee-mun Ong, “Dynamic Simulation of Electric Machinery using Matlab Simulink,” Prentice Hall PTR, 1998.

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APA Style

Liu Zhi Jian, Nguyen Le Minh Tri, Nguyen Le Thai, Phan Xuan Le. (2015). Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System. International Journal of Energy and Power Engineering, 4(1), 39-45. https://doi.org/10.11648/j.ijepe.20150401.16

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ACS Style

Liu Zhi Jian; Nguyen Le Minh Tri; Nguyen Le Thai; Phan Xuan Le. Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System. Int. J. Energy Power Eng. 2015, 4(1), 39-45. doi: 10.11648/j.ijepe.20150401.16

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AMA Style

Liu Zhi Jian, Nguyen Le Minh Tri, Nguyen Le Thai, Phan Xuan Le. Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System. Int J Energy Power Eng. 2015;4(1):39-45. doi: 10.11648/j.ijepe.20150401.16

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@article{10.11648/j.ijepe.20150401.16,
  author = {Liu Zhi Jian and Nguyen Le Minh Tri and Nguyen Le Thai and Phan Xuan Le},
  title = {Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System},
  journal = {International Journal of Energy and Power Engineering},
  volume = {4},
  number = {1},
  pages = {39-45},
  doi = {10.11648/j.ijepe.20150401.16},
  url = {https://doi.org/10.11648/j.ijepe.20150401.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20150401.16},
  abstract = {Switched reluctance motors (SRM) have a wide range of applications in industries due to the special properties of this motor. However, because of its dynamical nonlinearities, so the problems control of SRM is complex. This paper proposed an adaptive intelligent controller for SRM with the aim to improve the ripple of torque. First, we use a fuzzy logic controller to control switch-off angle, and then proposes a new controller by means of Adaptive Neural Fuzzy Inference (ANFIS). Simulation results are given to show the efficacy of the proposed method.},
 year = {2015}
}

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TY  - JOUR
T1  - Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System
AU  - Liu Zhi Jian
AU  - Nguyen Le Minh Tri
AU  - Nguyen Le Thai
AU  - Phan Xuan Le
Y1  - 2015/02/06
PY  - 2015
N1  - https://doi.org/10.11648/j.ijepe.20150401.16
DO  - 10.11648/j.ijepe.20150401.16
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 39
EP  - 45
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20150401.16
AB  - Switched reluctance motors (SRM) have a wide range of applications in industries due to the special properties of this motor. However, because of its dynamical nonlinearities, so the problems control of SRM is complex. This paper proposed an adaptive intelligent controller for SRM with the aim to improve the ripple of torque. First, we use a fuzzy logic controller to control switch-off angle, and then proposes a new controller by means of Adaptive Neural Fuzzy Inference (ANFIS). Simulation results are given to show the efficacy of the proposed method.
VL  - 4
IS  - 1
ER  -

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Author Information

Liu Zhi Jian

Faculty of Electric Power Engineering, Kunming University of Science and technology, Kunming City, Yunnan Province, China
Nguyen Le Minh Tri

Faculty of Electric Power Engineering, Kunming University of Science and technology, Kunming City, Yunnan Province, China
Nguyen Le Thai

Faculty of Electric and Electronic Engineering, Tuy Hoa Industrial College, Tuy Hoa City, Phu Yen Province, Vietnam
Phan Xuan Le

Faculty of Electric Power Engineering, Kunming University of Science and technology, Kunming City, Yunnan Province, China

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Plain Text BibTeX RIS

APA Style

Liu Zhi Jian, Nguyen Le Minh Tri, Nguyen Le Thai, Phan Xuan Le. (2015). Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System. International Journal of Energy and Power Engineering, 4(1), 39-45. https://doi.org/10.11648/j.ijepe.20150401.16

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ACS Style

Liu Zhi Jian; Nguyen Le Minh Tri; Nguyen Le Thai; Phan Xuan Le. Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System. Int. J. Energy Power Eng. 2015, 4(1), 39-45. doi: 10.11648/j.ijepe.20150401.16

Copy | Download

AMA Style

Liu Zhi Jian, Nguyen Le Minh Tri, Nguyen Le Thai, Phan Xuan Le. Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System. Int J Energy Power Eng. 2015;4(1):39-45. doi: 10.11648/j.ijepe.20150401.16

Copy | Download

@article{10.11648/j.ijepe.20150401.16,
  author = {Liu Zhi Jian and Nguyen Le Minh Tri and Nguyen Le Thai and Phan Xuan Le},
  title = {Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System},
  journal = {International Journal of Energy and Power Engineering},
  volume = {4},
  number = {1},
  pages = {39-45},
  doi = {10.11648/j.ijepe.20150401.16},
  url = {https://doi.org/10.11648/j.ijepe.20150401.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20150401.16},
  abstract = {Switched reluctance motors (SRM) have a wide range of applications in industries due to the special properties of this motor. However, because of its dynamical nonlinearities, so the problems control of SRM is complex. This paper proposed an adaptive intelligent controller for SRM with the aim to improve the ripple of torque. First, we use a fuzzy logic controller to control switch-off angle, and then proposes a new controller by means of Adaptive Neural Fuzzy Inference (ANFIS). Simulation results are given to show the efficacy of the proposed method.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Switching-off Angle Control for Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System
AU  - Liu Zhi Jian
AU  - Nguyen Le Minh Tri
AU  - Nguyen Le Thai
AU  - Phan Xuan Le
Y1  - 2015/02/06
PY  - 2015
N1  - https://doi.org/10.11648/j.ijepe.20150401.16
DO  - 10.11648/j.ijepe.20150401.16
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 39
EP  - 45
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20150401.16
AB  - Switched reluctance motors (SRM) have a wide range of applications in industries due to the special properties of this motor. However, because of its dynamical nonlinearities, so the problems control of SRM is complex. This paper proposed an adaptive intelligent controller for SRM with the aim to improve the ripple of torque. First, we use a fuzzy logic controller to control switch-off angle, and then proposes a new controller by means of Adaptive Neural Fuzzy Inference (ANFIS). Simulation results are given to show the efficacy of the proposed method.
VL  - 4
IS  - 1
ER  -

Copy | Download