Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Nonlinear Predictive Control with Multiple Models

다중 모델을 이용한 비선형 시스템의 예측제어에 관한 연구

  • 신승철 (한국전자통신연구원 정보통신원천기술연구소) ;
  • 변증남 (한국과학기술원 전자전산학과)
  • Published : 2001.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In the paper, we propose a predictive control scheme using multiple neural network-based prediction models. To construct the multiple models, we select several specific values of a parameter whose variation affects serious control performance in the plant. Among the multiple prediction models, we choose one that shows the best predictions for future outputs of the plant by a switching technique. Based on a nonlinear programming method, we calculate the current process input in the nonlinear predictive control system with multiple prediction models. The proposed control method is shown to be very effective when a parameter of the plant changes or the time delay, if it exists, varies. It is also shown that the proposed method is successfully applied for the control of suspension in a electro-magnetic levitation system.

본 논문에서는 신경회로망 기반의 다중 모델을 이용한 예측제어 방법에 간하여 기술한다. 플랜트의 특정한 피라미터 값들에 대해 다중의 모델을 구성하고, 이들 중 현재 시간에서 최적의 예측 값을 제공하는 모델을 스위칭 기법으로 선택한다. 선택된 모델의 예측 값을 기반으로 비선형 프로그래밍 방법으로 현재 시간에서의 제어 입력 값을 구하여 예측제어를 수행한다. 제안한 방법을 시간지연 값이 변하거나 매개변수 값이 가변하는 시스템에 적용하여 그 유용성을 보이고, 부하가 변동하는 자기부상열차 시스템의 부상제어에 이용한 모의 실험 결과를 보인다.

Keywords

References

  1. E.F. Camacho, F.R. Rubio, and F.M. Hughes, 'Self-tuning control of a solar plant with a distributed collector field', IEEE Control Systems, pp. 72-78, April 1992 https://doi.org/10.1109/37.126858
  2. D.W. Clake, 'Application of generalized predictive control to industrial processes', IEEE Control, Systems, pp. 49-55, April 1988 https://doi.org/10.1109/37.1874
  3. G. Geng and G.M. Geary, 'Application of a neural network-based RLS algorithm in the generalized predictive control of a nonlinear air-handling plant', IEEE Transactions on Control Systems Technology, vol. 5, pp. 439-445, 1997 https://doi.org/10.1109/87.595925
  4. M. Fischer, M. Schmidt, and K Kavsek-Biasizzo, 'Nonlinear predictive control based on the extraction of step response models from Takagi-Sugeno fuzzy systems', Proceedings of ACC, pp. 2878-2882, June 1992 https://doi.org/10.1109/ACC.1997.611983
  5. S.C. Ahn, Y.H. Kim, and W.H.Kwon, 'A fuzzy generalized predictive control using affine fuzzy predictors for nonlinear systems', Journal of Intelligent and Fuzzy Systems, vol. 6, pp. 185-207, 1998
  6. A. Draeger, S. Engell, and H. Ranke, 'Model predictive control using neural networks', IEEE Control Systems, vol. 15, pp. 61-66, 1995 https://doi.org/10.1109/37.466261
  7. M. Norgaard, P.H. Sorensen, and N.K.Poulsen, 'Intelligent predictive control of nonlinear processes using neural networks', Proceedings of IEEE Int. Symp. Intelligent Control, pp. 301-306, 1996 https://doi.org/10.1109/ISIC.1996.556218
  8. M.M. Gupta and N.K. Sinha, Intelligent control systems; theory and applications, IEEE Press, 1996
  9. P.K. Sinha, Electromagnetic Suspension dynamics and control, Peter Peregriunus Ltd., London, 1987
  10. E.F. Camacho and C. Bordons, Model predictive control in the process industry, Springer-Verlag, 1995
  11. X. He and H. Asada, 'A new method for identifying orders of I/O models for nonlinear dynamical systems', Proceedings of ACC, pp. 2520-2523, 1993
  12. S.C. Shin and Z. Bien, 'Constrained GA-based predictive control', Proceedings of IEEE Fall Conf., vol. 22, pp. 732-735, Nov. 1999
  13. L.O. Ljung, System identification: theory for the user, Prentice Hall, 1987
  14. D.W. Clarke, C. Mohtadi, and P.S. Tuffs, 'Generalized predictive control: Part I, and Part II', Automatica, vol. 23, pp. 137-160, 1987 https://doi.org/10.1016/0005-1098(87)90087-2
  15. J. Saint-Donat, N. Bhat, and T.J. McAvoy, 'Neural net based model predictive control', Int. J. Control, vol. 54, pp. 1453-1468, 1991
  16. S.C. Shin and S.B. Park, 'GA-based predictive control for nonlinear processes', Electronics Letters, vol. 34, pp. 1980-1981, 1998 https://doi.org/10.1049/el:19981368
  17. J.E. Dannis and J.J. More, 'Quasi-Newton methods, motivation and theory', SIAM Review, vol 19, pp. 46-89, 1977 https://doi.org/10.1137/1019005
  18. S.H. Lee, H.K. Sung J.T. Lim, and Z. Bien, 'On self-tuning control of electromagnetic levitation systems', Control Engineering Practice, vol. 8, pp. 749-756, 2000 https://doi.org/10.1016/S0967-0661(00)00005-8