Speed Estimation and Control of IPMSM Drive using NFC and ANN

NFC와 ANN을 이용한 IPMSM 드라이브의 속도 추정 및 제어

  • 이정철 (순천대 대학원 전기공학과) ;
  • 이홍균 (순천대 대학원 전기공학과) ;
  • 정동화 (순천대 정보통신공학부)
  • Published : 2005.06.01

Abstract

This paper proposes a fuzzy neural network controller based on the vector control for interior permanent magnet synchronous motor(IPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability This paper does not oかy presents speed control of IPMSM using neuro-fuzzy control(NFC) but also speed estimation using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Thus, it is presented the theoretical analysis as well as the analysis results to verify the effectiveness of the proposed method in this paper.

본 논문에서는 NFC(Neuro-Fuzzy Controller)와 ANN(Artificial Neural network) 제어기를 이용한 IPMSM의 속도 제어 및 추정을 제시한다. PI 제어기에서 나타나는 문제점을 해결하기 위하여 신경회로망과 퍼지제어를 혼합적용한 NFC를 설계한다. 신경회로망의 고도의 적응제어와 퍼지 제어기의 강인성 제어의 장점들을 접목한다. 다음은 ANN을 이용하여 IPMSM 드라이브의 속도 추정기법을 제시한다. 2층 구조를 가진 신경회로망에 BPA(Back Propagation Algorithm)를 적용하여 IPMSM 드라이브의 속도를 추정한다. 추정속도의 타당성을 입증하기 위하여 시스템을 구성하여 제어특성을 분석한다.

Keywords

References

  1. A. Consolim G. Scarcella and A. Testa, 'Sensorless control of PM synchronous motors at zero speed,' IEEE IAS Conf. Rec. Ann. Meet., pp. 1033-1040, 1999
  2. K. R. Shouse and D. G. Taylor, 'Sensorless velocity control of permanent magnet synchronous motors,' IEEE Trans. on CST, Vol. 6, No. 3, pp. 313-324, 1998
  3. B.K. Bose, 'Intelligent control and estimation in power electronics and drives,' Proc. IEEE International Electric Machines and Drives Conf. TA2-2.1-TA2-2.6, 1997
  4. T. L. Chen and Y. C. Wu, 'Design of integral variable structure controller and application to electrohydraulic velocity servo system.' Proc. Inst. Elect. Eng., Vol. 138, No. 5, pp. 439-444, 1991
  5. Y. R. Kim, S. K. Sul and M. H. Park, 'Speed sensorless vector control of induction motor using extended kalrnan filter,' IEEE Trans. IA, Vol. 30, No. 5, pp. 1225-1233, 1994
  6. B.K. Bose, 'Expert systems, fuzzy logic and neural network applications in power electronics and motion control,' Proc. of the IEEE, 82, pp.1303-1323, 1994
  7. D. H. Chung, 'Fuzzy control for high performance vector control of PMSM drive system,' KIEE, Vol. 47, No. 12, pp. 2171-2180, 1998
  8. A. K. Toh, E. P. Nowicki and F. Ashrafzadeh, 'A flux estimator for field oriented control of an induction motor using an artificial neural network,' IEEE IAS Conf. Rec. Ann. Meet., Vol. 1, pp. 585-592, 1994
  9. M. G. Simoes and B. K. Bose, 'Neural network based estimation of feedback signals for a vector controlled inducion motor drive,' IEEE Trans. IA, Vol. 31, No. 3, pp. 620-629, 1995
  10. M. T. Wishart and R. G. Harley, 'Identification and control of induction machines using neural networks, ' IEEE Trans. IA, Vol. 31, No. 3, pp. 612-619, 1995
  11. I. J. Leontaritis and S. A. Billings, 'Input-output parametric models for nonlinear systems,' Int. J. Contr., Vol. 41, pp. 303-344, 1985 https://doi.org/10.1080/0020718508961129