DOI QR코드

DOI QR Code

ANN Rotor Resistance Estimation of Induction Motor Drive using Multi-AFLC

다중 AFLC를 이용한 유도전동기 드라이브의 ANN 회전자저항 추정

  • Received : 2010.06.28
  • Accepted : 2011.02.23
  • Published : 2011.04.30

Abstract

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

Keywords

References

  1. B. K. Bose, “Modern power electronics and AC drives,” Englewood Cliffs, NJ, Prentice-Hall, 2002.
  2. I. Boldea and S. A. Nasar, “Electric drives,” New York: Taylor & Francis, 2006.
  3. R. D. Lorenz and D. B. Lawson, “A simplified approach to continuous on-line tuning of field oriented induction motor drives,” IEEE Trans. IA, vol. 26, no. 3, pp. 420-424, 1990. https://doi.org/10.1109/28.55972
  4. T. Rowan, R. Kerkman and D. Leggate, “A simple on-line adaptation for indirect field orientation of an induction machine,” IEEE Trans. IA, vol. 37, pp. 720-727, 1991.
  5. J. Holtz and T. Thimm, “Identification of the machine parameters in a vector-controlled induction motor drive,” IEEE Trans. IA, vol. 27, pp. 1111-1118, 1991. https://doi.org/10.1109/28.108462
  6. H. Sugimoto and S. Tamai, “Secondary resistance identification of an induction motor applied model reference adaptive system and its characteristics,” in IEEE IAS Ann. Meet. Conf. Rec., pp. 613-620, 1985.
  7. L. Zhen and L. Xu, “A mutual MRAS identification scheme for position sensorless field orientation control of induction motors,” in Proc. Conf. Rec. IEEE-IAS Annu. Meeting, pp. 159-165, 1995. https://doi.org/10.1109/IAS.1995.530297
  8. T. Noguchi, S. Kondo and I. Takahashi, “Field-oriented control of an induction motor with robust on-line tuning of its parameters,” IEEE Trans. IA, vol. 33, pp. 35-42, 1997. https://doi.org/10.1109/28.567074
  9. R. Marino, S. Persada and P. Valigi, “Adaptive input-output linearizing control of induction motors,” IEEE Trans. on AC, vol. 38, pp. 208-221, 1993. https://doi.org/10.1109/9.250510
  10. R. Marino, S. Persada and P. Tomei, “Global adaptive output feedback control of induction motors with uncertain rotor resistance,” IEEE Trans. AC, vol. 44, pp. 967-983, 1999. https://doi.org/10.1109/9.763212
  11. T. Matsuo and T. A. Lipo, “A rotor parameter identification scheme for vector controlled induction motor drives,” IEEE Trans. on IA, vol. 21, pp. 624-632, 1985. https://doi.org/10.1109/TIA.1985.349719
  12. L. C. Zaiand T. A. Lipo, “An extended kalman filter approach in rotor constant measurement in PWM inverter induction motor drives,” pp. 177-183, 1987.
  13. D. Atkinson, P. Acarnley and J. Finch, “Observer for induction motor drives,” IEEE Trans. IE, vol. 27, pp. 177-183, 1991.
  14. S. K. Mondal, J. O. P. Pinto and B. K. Bose, “A neural network based space-vector PWM controller for a three voltage-fed inverter induction motor drive,” IEEE Trans. IA, vol. 38, no. 3, pp. 660-669, 2002. https://doi.org/10.1109/TIA.2002.1003415
  15. J. C. Lee, H. G. Lee, Y. S. Lee, S. M. Nam and D. H. Chung, “Speed estimation and control of induction motor drive using hybrid intelligent control,” International Conference ICPE'04, no. 3, pp. 181-185, 2004.
  16. J. C. Lee, H. G. Lee, Y. S. Lee and D. H. Chung, “Sensorless vector control of induction motor drive using hybrid intelligent controller,” International Conference ICEMS'04, Conference no. PI-6(430-M09-053), 2004. [CD no. 2]
  17. H. G. Lee, S. M. Nam, J. S. Ko, J. S. Choi, J. C. Lee and D. H. Chung, “MTPA control of induction motor drive using fuzzy-neural networks controller,” ICCAS 2005, p. 134, 2005.