Modeling and Simulation of Position Estimation of Switched Reluctance Motor with Artificial Neural Networks
In the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised backpropagation learning algorithm are used in training of the ANN based position estimator. A 4-phase SRM have been used to verify the accuracy and feasibility of the proposed position estimator. Simulation results show that the proposed position estimator gives precise and accurate position estimations for both under the low and high level reference speeds of the SRM
[1] R. Krishnan, Switched Reluctance Motor Drives: Modeling, Simulation,
Analysis, Design, and Applications. Boca Raton, FL: CRC Press, Jun.
2001.
[2] E. Mese, "A rotor position estimator for switched reluctance motors
using CMAC", Energy Conversion and Management, 44 (2003) 1229-
1245.
[3] E. Mese and D. A. Torrey, "An approach for sensorless position
estimation for switched reluctance motors using artificial neural
networks," IEEE Trans. Power Electron., vol. 17, no. 1, Jan. 2002, pp.
66-75.
[4] M. Ehsani, and B. Fahimi, "Elimination of position sensors in switched
reluctance motor: State of the art and future trends," IEEE Trans. Ind.
Electron., vol. 49, no. 1, Feb. 2002, pp. 40-47.
[5] D.S. Reay, Y. Dessouky, and B.W. Williams, "The use of neural
networks to enhance sensorless position detection in switched reluctance
motors", IEEE International Conference on Systems, Man, and
Cybernetics, 1998, pp. 1774-1778.
[6] H. S. Ooi and T. C. Green, "Simulation of neural networks to sensorless
control of switched reluctance motor," in Proc. IEEE Power Electron.
Variable Speed Drives, Sep. 1998, pp. 281-286.
[7] D. S. Reay and B. W. Williams, "Sensorless position detection using
neural networks for the control of switched reluctance motors," in Proc.
IEEE Int. Conf. Control Appl., vol. 2, Aug. 1999, pp.1073-1077.
[8] T. Lachman, T. R. Mohamad, and S. P. Teo, "Sensorless position
estimation of switched reluctance motors using artificial neural
networks," in Proc. IEEE Int. Conf. Robot., Intell. Syst. Signal Process.,
vol. 1, Oct. 2003, pp. 220-225.
[9] B. Enayati, and S.M. Saghaiannejad, "Sensorless position control of
switched reluctance motors based on artificial neural networks", IEEE
ISIE 2006, July 9-12, 2006, Montreal, Quebec, Canada, pp. 2266-2271.
[10] W.S. Baik, M.H. Kim, N.H. Kim, and D.H. Kim, "Position sensorless
control system of srm using neural network," Proc. IEEE PESC, vol. 5,
Jun. 2004, pp. 3471-3475.
[11] S. Paramasivam, R. Arumugam, B. Umaniaheswari, "Accurate rotor
position estimation for switched reluctance motor using ANFIS",
Conference on Convergent Technologies for Asia-Pacific Region
TENCON 2003, Volume 4, 15-17 Oct. 2003 pp. 1493-1497.
[12] T. Shi, C. Xia, M. Wang and Qian Zhang, "Single neural PID control for
sensorless switched reluctance motor based on RBF neural network",
Proceedings of the 6th World Congress on Intelligent Control and
Automation, June 21-23, 2006, Dalian, China, pp. 8069-8073.
[13] S. Paramasivam, S. Vijayan, M. Vasudevan, R. Arumugam, and Ramu
Krishnan, "Real-time verification of AI based rotor position estimation
techniques for a 6/4 pole switched reluctance motor drive", IEEE
Transactions on Magnetics, vol. 43, no. 7, July 2007, pp. 3209-3222.
[14] C. Hudson, N:S. Lobo, R. Krishnan, "Sensorless control of single switch
based switched reluctance motor drive using neural network", The 30th
Annual Conference of the IEEE Industrial Electronics Society,
November 2-6, Busan, Korea, 2004, pp. 2349-2354.
[15] C. A. Hudson, N. S. Lobo, and R. Krishnan, "Sensorless Control of
Single Switch-Based Switched Reluctance Motor Drive Using
Neural Network", IEEE Transactions On Industrial Electronics,
vol. 55, no. 1, January 2008, pp. 321-329.
[16] O. Ustun, "Measurement and real-time modeling of inductance
and flux linkage in switched reluctance motors”, IEEE
Transactions on Magnetics, In press.
[17] O. Ustun, "A nonlinear full model of switched reluctance motor with
artificial neural network”, Energy Conversion and Management,
doi:10.1016/j.enconman.2009.05.025.
[1] R. Krishnan, Switched Reluctance Motor Drives: Modeling, Simulation,
Analysis, Design, and Applications. Boca Raton, FL: CRC Press, Jun.
2001.
[2] E. Mese, "A rotor position estimator for switched reluctance motors
using CMAC", Energy Conversion and Management, 44 (2003) 1229-
1245.
[3] E. Mese and D. A. Torrey, "An approach for sensorless position
estimation for switched reluctance motors using artificial neural
networks," IEEE Trans. Power Electron., vol. 17, no. 1, Jan. 2002, pp.
66-75.
[4] M. Ehsani, and B. Fahimi, "Elimination of position sensors in switched
reluctance motor: State of the art and future trends," IEEE Trans. Ind.
Electron., vol. 49, no. 1, Feb. 2002, pp. 40-47.
[5] D.S. Reay, Y. Dessouky, and B.W. Williams, "The use of neural
networks to enhance sensorless position detection in switched reluctance
motors", IEEE International Conference on Systems, Man, and
Cybernetics, 1998, pp. 1774-1778.
[6] H. S. Ooi and T. C. Green, "Simulation of neural networks to sensorless
control of switched reluctance motor," in Proc. IEEE Power Electron.
Variable Speed Drives, Sep. 1998, pp. 281-286.
[7] D. S. Reay and B. W. Williams, "Sensorless position detection using
neural networks for the control of switched reluctance motors," in Proc.
IEEE Int. Conf. Control Appl., vol. 2, Aug. 1999, pp.1073-1077.
[8] T. Lachman, T. R. Mohamad, and S. P. Teo, "Sensorless position
estimation of switched reluctance motors using artificial neural
networks," in Proc. IEEE Int. Conf. Robot., Intell. Syst. Signal Process.,
vol. 1, Oct. 2003, pp. 220-225.
[9] B. Enayati, and S.M. Saghaiannejad, "Sensorless position control of
switched reluctance motors based on artificial neural networks", IEEE
ISIE 2006, July 9-12, 2006, Montreal, Quebec, Canada, pp. 2266-2271.
[10] W.S. Baik, M.H. Kim, N.H. Kim, and D.H. Kim, "Position sensorless
control system of srm using neural network," Proc. IEEE PESC, vol. 5,
Jun. 2004, pp. 3471-3475.
[11] S. Paramasivam, R. Arumugam, B. Umaniaheswari, "Accurate rotor
position estimation for switched reluctance motor using ANFIS",
Conference on Convergent Technologies for Asia-Pacific Region
TENCON 2003, Volume 4, 15-17 Oct. 2003 pp. 1493-1497.
[12] T. Shi, C. Xia, M. Wang and Qian Zhang, "Single neural PID control for
sensorless switched reluctance motor based on RBF neural network",
Proceedings of the 6th World Congress on Intelligent Control and
Automation, June 21-23, 2006, Dalian, China, pp. 8069-8073.
[13] S. Paramasivam, S. Vijayan, M. Vasudevan, R. Arumugam, and Ramu
Krishnan, "Real-time verification of AI based rotor position estimation
techniques for a 6/4 pole switched reluctance motor drive", IEEE
Transactions on Magnetics, vol. 43, no. 7, July 2007, pp. 3209-3222.
[14] C. Hudson, N:S. Lobo, R. Krishnan, "Sensorless control of single switch
based switched reluctance motor drive using neural network", The 30th
Annual Conference of the IEEE Industrial Electronics Society,
November 2-6, Busan, Korea, 2004, pp. 2349-2354.
[15] C. A. Hudson, N. S. Lobo, and R. Krishnan, "Sensorless Control of
Single Switch-Based Switched Reluctance Motor Drive Using
Neural Network", IEEE Transactions On Industrial Electronics,
vol. 55, no. 1, January 2008, pp. 321-329.
[16] O. Ustun, "Measurement and real-time modeling of inductance
and flux linkage in switched reluctance motors”, IEEE
Transactions on Magnetics, In press.
[17] O. Ustun, "A nonlinear full model of switched reluctance motor with
artificial neural network”, Energy Conversion and Management,
doi:10.1016/j.enconman.2009.05.025.
@article{"International Journal of Electrical, Electronic and Communication Sciences:52022", author = "Oguz Ustun and Erdal Bekiroglu", title = "Modeling and Simulation of Position Estimation of Switched Reluctance Motor with Artificial Neural Networks", abstract = "In the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised backpropagation learning algorithm are used in training of the ANN based position estimator. A 4-phase SRM have been used to verify the accuracy and feasibility of the proposed position estimator. Simulation results show that the proposed position estimator gives precise and accurate position estimations for both under the low and high level reference speeds of the SRM
", keywords = "Artificial neural networks, modeling andsimulation, position observer, switched reluctance motor.", volume = "3", number = "9", pages = "1659-5", }
