대한전기학회논문지:전기기기및에너지변환시스템부문B (The Transactions of the Korean Institute of Electrical Engineers B)

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

신경망 외란관측기와 파라미터 보상기를 이용한 PMSM의 정밀 위치제어

Precision Position Control of PMSM Using Neural Network Disturbance observer and Parameter compensator

  • 고종선 (단국대학 전기전자컴퓨터공학과) ;
  • 진달복 (원광대학 전기전자 및 정보통신공학과) ;
  • 이태훈 (슈나이더일렉트릭코리아(주) 기술영업부)
  • 발행 : 2004.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

키워드

참고문헌

  1. D. W. Novotny and R. D. Lorentz 'Introduction to field orientation and high performance AC drives' IEEE-IAS Tutorial Course, 1986
  2. P. C. Krause, Analysis of electric machinery, McGraw-Hill, 1984
  3. K. J. Astrom and B. Wittenmark Computer controlled system, Prentice Hall, International, 1997
  4. J. S. Ko, J. H. Lee, S. k. Chung, and M. J. Youn 'A Robust Position Control of Brushless DC motor with Dead Beat Load Torque Observer' IEEE Transaction on Industrial Electronics, vol. 40, no. 5, pp. 512-520, 1993 https://doi.org/10.1109/41.238020
  5. C. T. Chen, Linear System Theory and Design, Holt, Rinehart and Winston, Inc., 1984
  6. C. Y. Huang, T. C. Chen, C. L. Huang 'Robust Control of Induction Motor with A Neural-Network Load Torque Estimator and A Neural-Network Identification' IEEE Transaction on Industrial Electronics, vol. 46, no. 5, pp 990-998, 1999 https://doi.org/10.1109/41.793348
  7. J. D. Landau, System Identification and Control Design. Englewood Cliffs, NJ, Prentice-Hall, 1990
  8. G. C. Goodwin, K. S. Sin, Adaptive Filtering Prediction and Control. Englewood Cliffs, NJ, Prentice-Hall, 1984
  9. Hornick K., Stinchcombe M., and White H., 'Multi-layer Feedforward Networks are Universal Approximators', Neural Networks, Vol. 2, 1989 https://doi.org/10.1016/0893-6080(89)90020-8
  10. Chen, F. 'Back-Propagation Neural Networks for Nonlinear Self-Tuning Adaptive Control', IEEE Control System Magazine, April, 1990 https://doi.org/10.1109/37.55123
  11. Jong Sun Ko and Myung Joong Youn, 'Robust Digital Position Control of BLDD Motors using Neural Network with State Feedback,' Proc. of the 3rd. International Workshop on Advanced Motion Control, pp. 852-861, March, 1994
  12. Jong-Sun Ko, Tae-Ho Lee, Chil-Hwan Cheon, and Sang-Seol Lee, Precision Speed Control of PMSM Using Disturbance Observer and Parameter Compensator KIPE, Vol. 6 No.1, pp98-106, Feb., 2001
  13. A. Krzyzak, E. Rafajlowicz, M. Pawlak 'Moving Average Restoration of Bandlimited Signals from Noisy Observations', IEEE Trans. on signal processing, Vol. 45, No. 12, 1997 https://doi.org/10.1109/78.650257