DOI QR코드

DOI QR Code

A DC Motor Speed Control by Selection of PID Parameter using Genetic Algorithm

  • Yoo, Heui-Han (Korea Maritime University) ;
  • Lee, Yun-Hyung (Korea Maritime University, College of Maritime Sciences, Department of Mechatronics Engineering)
  • Published : 2007.05.31

Abstract

The aim of this paper is to design a speed controller of a DC motor by selection of a PID parameters using genetic algorithm. The model of a DC motor is considered as a typical non-oscillatory, second-order system, And this paper compares three kinds of tuning methods of parameter for PID controller. One is the controller design by the genetic algorithm. second is the controller design by the model matching method third is the controller design by Ziegler and Nichols method. It was found that the proposed PID parameters adjustment by the genetic algorithm is better than the Ziegler & Nickels' method. And also found that the results of the method by the genetic algorithm is nearly same as the model matching method which is analytical method. The proposed method could be applied to the higher order system which is not easy to use the model matching method.

Keywords

References

  1. M.A. Rahman, and P. Zhou, 'Accurate determination of permanent magnet motor parameters by digital torque angle measurement', Jounal of Applied Physics, 76(10), pp. 6868 6870, 1994 https://doi.org/10.1063/1.358096
  2. M.A. Rahman, and P. Zhou, 'Analysis of brushless permanent magnet synchronous motors', IEEE Trans. Industrial Electronics, 43(2), pp. 256-267, 1995 https://doi.org/10.1109/41.491349
  3. P. Zhou, M.A. Rahman, and M.A. Jabbar, 'Field circuit analysis of permanent magnet synchronous motors', IEEE Trans. Magnetics, 30(4), pp. 1350-1358, 1994 https://doi.org/10.1109/20.305531
  4. B.G. Hu, George K.I. Mann, and R.G. Gosine, 'A systematic study of fuzzy PID controllers', IEEE Trans. on fuzzy systems, 9(5), pp. 699-712, 2001 https://doi.org/10.1109/91.963756
  5. W.S. Park, I.H. Ryu and S.S. Lee, 'A study on the DC motor speed control using neural network PID controller', Journal of the research institute of technology development, Wonkwang University, 23, pp. 41-45, 2003
  6. J.O. Hang, F.L. Lewis, 'Neural network predictive control for nonlinear dynamics systems with time delay', IEEE Trans. Neural networks, 4(2), pp. 377-389, 2003
  7. T. Iwasaki and A. Morita, 'Fuzzy auto tuning for PID controller with model classification', in Proceedings, NAFIPs '90, Toronto, Canada, pp. 90-93, 1990
  8. T. Kitamori, 'A method of control system design based on partial knowledge about controlled process', Transactions, SCIE Japan, 15, pp. 549-555, 1979
  9. T. Kitamori, 'Design of PID control system', Measurement and Control, 19, pp. 382-391,1980
  10. B. C. Kuo, Automatic control systems, 7th edition, Englewood Cliffs, NJ: Prentic Hall, 1995
  11. S. M. Shinner, Modern control system theory and application, Addison Wesley, Boston, 1978
  12. Y. Takahashi, M. J. Rabins, and D. M. Auslander, Control and dynamic systems, Menlo Park, NJ: Addison Wesley, 1970
  13. J.G. Ziegler and N.B. Nichols, 'Optimal settings for automatic controllers', Transactions of A.S.M.E., 14, pp. 759-768, 1942
  14. G. Jin, Genetic algorithms and their applications, Kyo Woo Sa, 2004
  15. Rockwell Automation, http://www.reliance.com/cgibin/dcexpolde.pl?T18R1010, 1999
  16. DC motors, http://www.reliance.com/pff/catalogs/imc 2004/dc motors med.pdf, 2004