Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Transient Response Improvement of Multiple Model/Controller IMC Using Recurrent Neural Networks

재귀신경망을 이용한 다중모델/제어기 IMC의 과도 응답 개선

  • O, Won-Geun (Dept.of Information Communication Engineering, Sunchon National University) ;
  • Jo, Seong-Eon (Dept.of Information Communication Engineering, Sunchon National University) ;
  • So, Ji-Yeong (Dept.of Information Communication Engineering, Sunchon National University)
  • 오원근 (순천대학교 정보통신공학부) ;
  • 조성언 (순천대학교 정보통신공학부) ;
  • 소지영 (순천대학교 정보통신공학부)
  • Published : 2001.07.01
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Abstract

The Multiple Model/Controller IMC(MMC-IMC) is a model-based control method which uses a set of model/controller pairs rather than a single model/controller to handle all possible operating conditions in the IMC control structure. During operation, one model/controller pair that best fit, for current plant situation is chosen by the switching algorithm. The major drawback of the switching controller is the bad transient performance due to the model error and the use fo linear controller for nonlinear plants. In this paper, we propose a method that transient response of the MMC-IMC using two recurrent neural networks. Simulation result shows that the proposed method represents better performance than the usual MMC-IMC`s.

Keywords

References

  1. W. J. Rugh and J. S. Sharnma, 'Research on gain scheduling,' Automatica, 36, pp. 1401-1425, 2000 https://doi.org/10.1016/S0005-1098(00)00058-3
  2. D. Liberzon and A. S. Morse, 'Basic problems in stability and design of switched systems,' IEEE Control Systems, pp. 59-70, Oct., 1999 https://doi.org/10.1109/37.793443
  3. K. S. Narendra and J. Balakrishnan, 'Adaptive control using multiple models,' IEEE Trans. on Automatic Control, vol. 42, no. 2, pp. 171-187, 1997 https://doi.org/10.1109/9.554398
  4. Edited by R. Murray-Smith and T. A. Johansen, Multiple Model Approaches to Modeling and Control, Taylor & Francis, 1997
  5. M. Morari and E. Zafiriou, Robust Process Control, Prentice Hall, 1989
  6. 오원근, 서병설, '다중 모델, 제어기, 스위칭을 이용한 비선형 플랜트의 IMC 제어기 설계,' 대한전자공학회지, 33 권, 11 호, pp. 22-30, 11, 1996
  7. C. G. Economou, M. Morari, and B. O. Palsson, 'Internal model control. 5. extension to nonlinear systems,' Ind. Eng. Chem. Process Des. Dev., 25, pp. 403-411, 1986 https://doi.org/10.1021/i200033a010
  8. M. Vidyasagar, Nonlinear Systems Analysis, Prentice Hall, 1993
  9. Won-Geun Oh and Byung-Suhl Suh, 'An on-iine learning algorithm for recurrent neural networks using variational methods,' Proc. of the 40th MWSCAS, pp. 659-662, 1997
  10. W. J. Rugh, 'Design of nonlinear PID controllers,' AIChE Journal, vol. 33, no. 10, pp. 1738-1742, Oct., 1987 https://doi.org/10.1002/aic.690331019