전력전자학회논문지 (The Transactions of the Korean Institute of Power Electronics)

  • 제11권1호
  • /
  • Pages.29-37
  • /
  • 2006
  • /
  • 1229-2214(pISSN)
  • /
  • 2288-6281(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
권호 표지

LM-FNN 제어기에 의한 IPMSM의 고성능 속도제어

High Performance Speed Control of IPMSM with LM-FNN Controller

  • 남수명 (순천대 전기전자정보통신공학부) ;
  • 최정식 (순천대 전기전자정보통신공학부) ;
  • 정동화 (순천대 전기전자정보통신공학부)
  • 발행 : 2006.02.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 LM-FNN(learning Mechanism-Fuzzy Neural Network) 제어기를 이용하여 IPMSM 드라이브의 고성능 속도를 제어한다. 고성능제어를 위하여 신경회로망과 퍼지제어를 혼합 적용한 FNN을 설계한고 더욱 성능을 개선하기 위하여 학습 메카니즘을 이용하여 FNN 제어기의 파라미터를 갱신시킨다. 그리고 ANN(Artificial Neural Network)을 이용하여 IPMSM 드라이브의 속도 추정기법을 제시한다. 추정속도의 타당성을 입증하기 위하여 시스템을 구성하여 제어특성을 분석한다. 그리고 추정된 속도를 지령속도와 비교하여 전류제어와 공간벡터 PWM을 통하여 IPMSM의 속도를 제어한다. 본 연구에서 제시한 LM-FNN과 ANN 제어기의 제어특성과 추정성능을 분석하고 그 결과를 제시한다.

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network 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. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.

키워드

참고문헌

  1. G. R. Slemon, 'Electric machines and drives,' Reading, MA: Addison-Wesley, 1992
  2. T. J. E. Miller, 'Brushless permanent magnet and reluctance motor drives,' Oxford, U. K: Clarendon, 1989
  3. K.J. Astron and B. Wittenmark, 'Adaptive control,' Addison-Wesley, 1989
  4. D. H. Chung, 'Fuzzy control for high performance vector control of PMSM drive system,' KIEE, Vol. 47, No. 12, pp. 2171-2180, 1998
  5. Y. Tang and L. Xu, 'Fuzzy logic application for intelligent control of a variable speed drive,' IEEE Trans. EC, Vol. 9, pp. 679-685, 1994
  6. K. S. Narendra and K. Parthasarthy, 'Identification and control of dynamical system using neural network,' IEEE Trans. Neural Networks, Vol. 1, No.1, pp. 4-27, 1990 https://doi.org/10.1109/72.80202
  7. Cybenko, 'Approximations by superposition of a sigmoidal function,' Mathematics of Contr., Signals and Syst., Vol. 2, pp. 303-314, 1989 https://doi.org/10.1007/BF02551274
  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. C. Schauder, 'Adaptive speed identification for vector control of induction motors,' IEEE Trans. on IA, pp. 1054-1061, 1992
  12. F. Z. Feng, T. Fukao, 'Robust speed identification for speed sensorless vector control of induction motors,' IEEE Trans. on IA, Vol. 30, No.5, pp. 1234-1240, 1994
  13. H. Kubota and K. Matsuse, 'Speed sensorless field oriented control of induction motor with rotor resistance adaption,' IEEE Trans. on IA, Vol. 30, No. 5, pp. 1219-1224, 1994