Hybrid Induction Motor Control Using a Genetically Optimized Pseudo-on-line Method

  • Lee, Jong-seok (Department of Radiotechnology, Wonkwang Health Science College) ;
  • Jang, Kyung-won (School of Electrical & Electronic Eng., Wonkwang Universit) ;
  • J. F. Peters (Department of Electrical and Computer Eng., University of Manitob) ;
  • Ahn, Tae-chon (School of Electrical & Electronic Eng., Wonkwang University)
  • Published : 2004.07.01

Abstract

This paper introduces a hybrid induction motor control using a genetically optimized pseudo-on-line method. Optimization results from the use of a look-up table based on genetic algorithms to find the global optimum of an unconstrained optimization problem. The approach to induction motor control includes a pseudo-on-line procedure that optimally estimates parameters of a fuzzy PID (FPID) controller. The proposed hybrid genetic fuzzy PID (GFPID) controller is applied to speed control of a 3-phase induction motor and its computer simulation is carried out. Simulation results show that the proposed controller performs better than conventional FPID and PID controllers. The contribution of this paper is the introduction of a high performance hybrid form of induction motor control that makes on-line and real-time control of the drive system possible.

References

  1. W. C. Dangherity , B. Rathakrishnan and J. Ten, 'Performance evaluation of a self-tuning fuzzy controller', IEEE Department of Computer Science Texas A & M University College Station TX 77843, pp. 389-397, 1992
  2. S. H. Chae, 'An induction motor drive using an auto-tuning fuzzy PID control algorithm', Chung-Ang University , Master's thesis, 1992
  3. T. Takagi and M. Sugeno, 'Fuzzy identification of system and its applications to modeling and control', IEEE Transactions on SMC-15, no.1 Jan./Feb., pp.116-132 , 1985
  4. B. K. Bose, 'Power Electronics and AC Drivers,' Prentice-Hall Book, 1986
  5. R. Caponetto, L. Fortuna, S. Graziani and M. Xibilia, 'Genetic algorithms and applications in system engineering: a Survey', Trans. Inst. Meas. Control, vol. 15, pp. 143-156, 1993
  6. P. C. Krause, 'Analysis of Electric Machinery', MeGraw-Hill Book, 1987
  7. P. Vas, 'Vector Control of AC Machines' Clarendon Press. Oxford. 1990
  8. M. Mizumoto, 'Realization of PID control by fuzzy control method', IEEE Division of Information and Computer Sciences, pp. 709-715, 1992
  9. M. Moallem, B. Mirzarian, O. A. Mohammed and C. Lucas, 'Multi-objective genetic-fuzzy optimal design of PI controller in the indirect field oriented control of an induction motor', IEEE Transactions on Magnetics, vol. 37, no.5, pp. 3608-3612, September,2001
  10. Y. F. Li and C. C. Lau, 'Development of Fuzzy Algorithms for Servo Systems', IEEE Control System Magazine, Apr., pp.65-71, 1989
  11. Y. Yoshinari, W. Pedrycz and K. Hirota, 'Construction of fuzzy models through clustering techniques' , Fuzzy sets Syst., vol. 54, no. 2. pp. 157-165,1993
  12. H. A. Malki, H. Li and G. Chen, 'New design and stability analysis of fuzzy proportional-derivative control system' IEEE Transactions on Fuzzy Systems, vol. I, no. 6, pp. 245-254, November 1994
  13. L. Zheng, 'A practical guide to tune of proportional and integral (PI) Like fuzzy controller', IEEE Yamatake-Honey Co. Report, Japan, 1992
  14. J. H. Holland, 'Adaptation in Neural and Artificial Systems', Ann Arbor, MI: University of Michigan Press Book,1975
  15. B. Hu, G. K. I. Mann and R. G. Gosine, 'New methodology for analytical and optimal design of fuzzy PID controller' , IEEE Transactions on Fuzzy Systems, vol. 7, no. 5, October, pp. 521-539,1999
  16. J. Cleland and W. Turner, 'Fuzzy logic control of AC induction motor', IEEE Research Tringle Institute, pp. 843-850, 1992