Journal of the Institute of Electronics Engineers of Korea CI (전자공학회논문지CI)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

A New Method of Adaptive Fuzzy Control System Using Genetic Algorithms

유전자 알고리즘을 이용한 적응 퍼지 제어 시스템의 새로운 방법

  • Chang, Won-Bin (Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Kim, Dong-Il (Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Kwon, Key-Ho (Electrical and Computer Engineering, Sungkyunkwan University)
  • 장원빈 (성균관대학교 전기전자 및 컴퓨터공학부) ;
  • 김동일 (성균관대학교 전기전자 및 컴퓨터공학부) ;
  • 권기호 (성균관대학교 전기전자 및 컴퓨터공학부)
  • Published : 2001.03.25
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes a new method of Genetic Algorithms for Adaptive Fuzzy Control System. Previous works using a Multi-population Genetic Algorithm have divided chromosome into two components, rule sets and membership functions. However, in this case bad rule sets disturb optimization in good rule sets and membership functions. A new method for a Multi population Genetic Algorithm suggests three components, good rule sets, bad rule sets, and membership functions. To show the effectiveness of this method, fuzzy controller is applied to a Truck Backing Problem. Results of the computer simulation show good adaptation of the proposed method.

본 논문은 적응 피지 제어 시스템에 있어 유전자 알고리즘에 대한 새로운 방법을 제안한다. 다중개체군 유전자 알고리즘을 이용한 이전의 논문은 염색체를 두부분(제어규칙과 소속함수)으로 분할하였다. 그러나 이런 경우 좋지 못한 제어규칙은 좋은 제어규칙과 잘 진화된 소속함수의 최적화를 방해한다. 다중개체군 유전자 알고리즘에 대한 새로운 방법은 염색체를 세부분(좋은 제어규칙, 좋지 못한 제어규칙 및 소속함수)으로 분할하는 것이다. 이 방법에 대한 효율성을 입증하기 위해 트럭 배킹 문제에 적용하였다. 시뮬레이션 결과 다중개체군 유전자 알고리즘에 대한 제안된 방법이 좋은 적응성을 보여 주었다.

Keywords

References

  1. C. D. Sousa and B. K. Bose, 'A Fuzzy Set Theory Based Control of a Phase Controlled Converter DC Machine Drive', IEEE, Trans. on Industry Applications, Vol. 30, No. 1, pp.34-44, 1994 https://doi.org/10.1109/28.273619
  2. Z. Y. Zhao, M. Tomizuka, and S. Isaka, 'Fuzzy Gain Scheduling of PID Controllers', IEEE Trans. Syst. Man Cybern., Vol. 23, No. 5, pp. 1392-1398, 1998 https://doi.org/10.1109/21.260670
  3. R. Ketata, D. De Geest, and A. Titli, 'Fuzzy Controller: Design, Evaluation, Parallel and Hierarchical Combination with a PID Controller', Fuzzy Sets and System, Vol. 71, pp. 113-129, 1995 https://doi.org/10.1016/0165-0114(94)00189-E
  4. C. C. Lee, 'Fuzzy Logic in Control Systems: Fuzzy Logic Controller Part I, II', IEEE Trans. Syst. Man Cybern., Vol. 20, No. 2, pp. 404-435, Mar./Apr. 1990 https://doi.org/10.1109/21.52551
  5. S. M. Smith and D. J. Corner, 'Automated Calibration of a Fuzzy Logic Controller Using a Cell State Space Algorithms', IEEE Control Syst. Mag., pp. 18-28, Aug. 1991 https://doi.org/10.1109/37.90533
  6. J. A. Bernard, 'Use of a Rule-based System for Process Control', IEEE Control Syst. Mag., pp. 3-13, Oct. 1988 https://doi.org/10.1109/37.7735
  7. Han-Xiong Li and H. B. Gatland, 'A New Methodology for Designing a Fuzzy Logic Controller', IEEE Trans. Syst. Man Cybern., Vol. 25, No. 3, pp. 505 512, 1995 https://doi.org/10.1109/21.364863
  8. Abdollah Homaifar and Ed McCormick, 'Simultancous Design of Membership Funtions and Rule Sets for Fuzzy Controllers Using Genetic Algorithms', IEEE Transactions on Fuzzy Systems, Vol. 3, No.2, May 1995
  9. S. Shao, 'Fuzzy Self organizing Controller and its Applications for Dynamic Processes', Fuzzy Sets and Syst., Vol. 26, pp. 151-164, 1988 https://doi.org/10.1016/0165-0114(88)90205-9
  10. D. Park, A. Kandel, and G. Langholz, 'Genetic-based New Fuzzy Reasoning Models with Application to Fuzzy Control', IEEE Trans. Syst. Man Cybern., Vol. 24, No. 1, pp. 39-47, 1994 https://doi.org/10.1109/21.259684
  11. C. D. Sousa and B. K. Bose, 'A Fuzzy Set Theory Based Control of a Phase Controlled Converter DC Machine Drive', IEEE, Trans. on Industry Applications, Vol. 30, No. 1, pp.34 44, 1994 https://doi.org/10.1109/28.273619
  12. Gyula Mester, Szilveszter Pletl, Attila Nemes and Tibor Mester, 'Structure Optimization of Fuzzy Control Systems by Multi-population Genetic Algorithm', EUFIT '98, September 7-10, 1998