DOI QR코드

DOI QR Code

Implementation of the Mass Flow Controller using Adaptive PID

적응 PID를 이용한 질량 유량 제어기 구현

  • Published : 2007.01.01

Abstract

The MFC(Mass Flow Controller) is an equipment that measures and controls mass flow rates of fluid. Most of the HFC system is still using the PID algorithm. The PID algorithm shows superior performance on the MFC system. But the PID algorithm in the MFC system has a few problems as followed. The characteristic of the MFC system is changed according to the operating environment. And, when the piezo valve that uses the control valve is assembled in the MFC system, a coupling error is generated. Therefore, it is very difficult to find out the exact parameters of MFC system. In this paper, we propose adaptive PID algorithm in order to compensate these problems of a traditional PID algorithm. The adaptive PID algorithm estimates the parameters of MFC system using LMS(Least Mean Square) algorithm and calculates the coefficients of PID controller. Besides, adaptive PID algorithm shows better transient response because adaptive PID algorithm includes a feedforward. And we implement MFC system using proposed adaptive PID algorithm with self-tuning and Ziegler and Nickels's method. Finally, comparative analysis of the proposed adaptive PID and the traditional PID is shown.

Keywords

References

  1. 사단법인 일본계량기기공업연합회, 유량계측 A to Z, 자동제어계측사, pp. 171-180, 1997
  2. 최성현, 가스 측정용 질량 유량계의 원리.구조와 적용, 제어계측, 자동제어계측사, pp. 37-39. 2003. 11
  3. Y. Isoda, 정온도차 제어방식 열식 질량 유량계, 제어계측, 자동제어계측사, pp. 86-89, 2001. 11
  4. Piezosystem inc., http://www.piezo.com/
  5. G. Mohamed, Flow Control, Cambridge University Press. 2000
  6. J. M. T. A. Han, L. Willem, and B. Reinder, 'Modeling piezoelectric actuators,' IEEF/ASME Trans. on Mezhatronics, vol. 5, no. 4, pp. 331-341, Dec. 2002
  7. 'Self-learning fuzzy logic system for in situ, in-process diagnostics of mass flow controller (MFC)' IEEE Trans, Semi. Manufacturing, vol. 7, no. I, Feb. 1994
  8. F. Nadi, A. M. Agogino, and D. A. Hodges, 'Use of influence diagrams and neural networks in modeling semiconductor manufacturing processes,' IEEE Trans. on Semi. Manufacturing, vol. 4, no. 1, pp. 52-58, Feb. 1991 https://doi.org/10.1109/66.75852
  9. 이명의, 정원철, '하이브리드형 질량 유량 제어기의 설계 및 실현,' 산학 기술 학회 논문지 vol. 4, no. 2, pp. 63-70, 2003
  10. 이석기, 이연정, 이승하, 'MFC의 퍼지 제어기 구현,' 퍼지 및 지능시스템학회 논문지, vol. 14, no. 5, pp. 648-654, 2004
  11. R N. Bateson, Introduction to Control System Technology, Prentice Hall, Inc., pp. 118-140
  12. C. L. Phillips and H. T. Nagle, Digital Control System Analysis and Design, Prentice Hall, Inc., pp. 404-413, 1995
  13. S. Haykin, Adaptive Filter Theory, Prentice Hall, Inc., pp. 610-635, 2002
  14. M. Hamdan and Z. Gao, 'A novel PID controller for pneumatic proportional values with hysteresis,' IEEE, pp. 1198-1201, 2000
  15. P. Ge and M. Jouaneh, 'Modeling hysteresis in piezoceramic actuators,' Precision Engineering, vol. 17, pp. 211-221, 1995 https://doi.org/10.1016/0141-6359(95)00002-U
  16. H. Kaoru and E. Masayoshi, 'Stainless steel-based integrated mass-flow controller for reactive and corrosive gases.,' Sensors and Actuators, pp. 33-38, 2002
  17. J. Schuurmans and T. Jones, 'Control of mass flow in a hot strip mill using model predictive control.,' Proc. of the 2002 IEEE int. Conf. on Control Applications, pp. 18-20, 2002