Precision Control of a Torque Standard Machine Using Fuzzy Controller

퍼지제어기를 이용한 토크 표준기의 정밀제어

  • Kim, Gab-Soon (Dept. of Control and Instrumentation Engineering, Graduate School of Gyeongsang National University) ;
  • Kang, Dae-Im (Korea Reserch Institute of Standards and Science)
  • 김갑순 (경상대학교 제어계측공학과, 생산기술연구소) ;
  • 강대임 (한국표준과학연구원 물리표준부)
  • Published : 2001.07.01

Abstract

This study describes the precision control of the torque standard machine using a self-tuning fuzzy controller. The torque standard machine should generate the accurate torque for calibrating a torque sensor. In order to reduce the relative expanded uncertainty of the torque standard machine, when a weight is hanged to the end of the torque arm for generating the torque, the sloped torque arm should be accurately controlled to the horizontal level. If the slope of the torque arm is larger from the inaccurate control, the uncertainty of the torque standard machine due to control will be larger. This applies the inaccurate torque to a torque sensor to calibrate, and the measuring error of the torque sensor generate from it. Therefore the torque arm of the torque standard machine is accurately controlled. In this paper, the self-tuning fuzzy controller was designed using a fuzzy theory, and the torque arm of the torque standard machine was accurately controlled. The control gain of the fuzzy controller, that is the membership function size of the error, the membership function size of the error change and the membership function size of the controller were determined from the self-tuning. The control results of the torque standard machine were the overshoot within 0.0076mm, the rise time within 16.70sec and the steady state error within 0.0076mm.

Keywords

References

  1. Lee C.C., 'Fuzzy Logic in Control Systems : Fuzzy Controller-Part 1,' IEEE Transactions on Systems, Man and Cyberhetics, Vol. 20, No. 2, pp. 404-418, 1990 https://doi.org/10.1109/21.52551
  2. Lee C.C., 'Fuzzy Logic in Control Systems : Fuzzy Controller-Part 2,' IEEE Transactions on Systems, Man and Cyberhetics, Vol. 20, No. 2, pp. 419-435, 1990 https://doi.org/10.1109/21.52552
  3. Yasunobu S., S. Miyamoto, and H. Ihara, 'Fuzzy control for automatic train operation system,' Proc. 4th IFAC/IFIPIFORS Int. Congress on Control in Transportation Systems, Baden-Baden, April, pp. 230-239, 1983
  4. Yasunobu S., and T. Hasegawa, 'Evaluation of an automatic container crane operation system based on predictive fuzzy control,' Control theory Adv. Technol., Vol. 2, No. 3, pp. 419-432, 1986
  5. Fujitec F., 'FLEX-8800 series elevator group control system,' Fujitec Co., Ltd., Osaka, Japan, pp. 156-163, 1988
  6. Kinoshita M., T. Fukuzaki, T. Satoh, and M. Miyake, 'An automatic operation method for control rods in BWR plants,' Proc. Specialists' Meeting on In-Core Instrumentation and Reactor Core Asseement, Cadarache, France, pp. 234-243, 1988
  7. Kasia Y., and Morimoto Y., 'Electronically controlled continuously variable transmission,' Proc. Int. Congress on Transportation Electronics, Dearborn, MI, pp. 29-44, 1988
  8. Jung C.H., and Ham C.S., 'A real-time self-tuning fuzzy controller through scaling factor adjustable for the steam generator of NPP,' Fuzzy Sets and Systems, 74, pp. 53-60, 1995 https://doi.org/10.1016/0165-0114(95)00035-J
  9. Maeda M., Murakami and S., 'A self-tuning fuzzy controller,' Fuzzy Sets and Systems, 51, pp. 29-40, 1992 https://doi.org/10.1016/0165-0114(92)90073-D
  10. 최승민, 김훈모, 'Adaptive Dual Fuzzy 알고리즘을 이용한 고층 빌딩의 엘리베이터 군 제어에 관한 연구,' 한국정밀공학회, 제 18 권 4 호, pp. 112-120, 2001
  11. OIML, 'Guide to the expression of uncertainty in measurement,' OIML, pp. 9-28, 1993
  12. Jong N. S.et al, KRISS, 'Guide to the Expression of Uncertainty in Measurement(KRISS-98-096-SP),' KRISS, pp. 17-40, 1998