Journal of Mechanical Science and Technology

  • 제17권2호
  • /
  • Pages.187-196
  • /
  • 2003
  • /
  • 1738-494X(pISSN)
  • /
  • 1976-3824(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
권호 표지

Design of FLC for High-Angle-of-Attack Flight Using Adaptive Evolutionary Algorithm

  • Won, Tae-Hyun (Department of Electrical Engineering, Dongeui Institute of Technology) ;
  • Hwang, Gi-Hyun (Department of Electrical Engineering, Pusan National University) ;
  • Park, June-Ho (Department of Electrical Engineering, Pusan National University) ;
  • Lee, Man-Hyung (School of Mech. Eng. & ERC/Net Shape and Die Manufacturing, Pusan National University)
  • 발행 : 2003.02.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, a new methodology of evolutionary computations - An Adaptive Evolutionary Algorithm (AEA) is proposed. AEA uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations : global search capability of GA and local search capability of ES. In the reproduction procedure, the proportions of the population by GA and ES are adaptively modulated according to the fitness. AEA is used to. designing fuzzy logic controller (FLC) for a high-angle-of-attack flight system for a super-maneuverable version of F-18 aircraft. AEA is used to determine the membership functions and scaling factors of an FLC. The computer simulation results show that the FLC has met both robustness and performance requirements.

키워드

참고문헌

  1. Adams, R. J., Buffington, J. M. and Banda, S. S., 1994, 'Implementation of a Full-Envolope Controller for a High-Performance Aircraft,' AIAA J. of Guidance, Control, and Dynamics, Vol. 17, No. 3, pp. 578-580 https://doi.org/10.2514/3.21236
  2. Arabas, J., Michalewicz, Z. and Mulawka, J., 1994, 'GAVaPS-a Genetic Algorithm with Varying Population Size,' IEEE International Conference on Evolutionary Computation, pp. 73-78 https://doi.org/10.1109/ICEC.1994.350039
  3. Cho, S., 1993, 'Nonlinear Adaptive Control Law of Highly Maneuverable High Performance Aircraft,' Ph. D. Dissertation, Oregon State University, Dept. of mechanical Engineering
  4. Etkins, B., 1982, Dynamics of flight : Stability and control, John Wiley & Sons
  5. Fogel, D. B., 1995, 'Evolutionary Computation : Towards New Philosophy of Machine Intelligence,' IEEE Press, Piscataway, NJ, pp. 67-101
  6. Fogel, D. B., Fogel, L. J. and Atmas, J. W., 1991, 'Meta-Evolutionary Programming,' Proceedings 2sth Asilomar Conference on Systems, Signals and Computers, pp. 540-545 https://doi.org/10.1109/ACSSC.1991.186507
  7. Gen, M. and Cheng, R., 1997, Genetic Algorithms & Engineering Design, A Wiley-Interscience Publication, pp. 42-67
  8. Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley publishing Company, INC., pp. 1-59
  9. Gong, D., Yamazaki, G. and Gen, M, 1996, 'Evolutionary Program for Optimal Design of Material Distribution System,' IEEE International Conference on Evolutionary Computation, pp. 139-143 https://doi.org/10.1109/ICEC.1996.542349
  10. Juang, C. F., Lin, J. Y. and Lin, C. T., 2000, Genetic Reinforcement Learning Through Symbiotic Evolution for Fuzzy Controller Design,' IEEE Transactions on Systems Man and Cybernetics, Part B, Vol. 30, No. 2, pp. 290-302 https://doi.org/10.1109/3477.836377
  11. Lee, A. W. and Hedrick, J. K., 1994, 'Application of Approximation I/O Linearization to Aircraft Flight Control,' ASME J. of Dynamics system Measurement and Control, Vol. 116, pp. 429-436 https://doi.org/10.1115/1.2899238
  12. Michalewicz, Z., 1992, Genetic Algorithms+Data Structures=Evolution Programs, Springer-Verlag, pp. 119-130
  13. Mohler, R. R. and Cho, S., Koo, C. S. and Zakrzewski, R. R., 1993, 'Nonlinear Stability and Control Study of Highly Maneuverable High Performance Aircraft,' OSU-ECE Report NASA 9201, Corvallis, OR
  14. Ostroff, A. J., 1992, 'High-Alpha Application of Variable Gain Output Feedback Control,' AIAA J. of Guidance, Control, and Dynamics, Vol. 15, No. 2, pp. 491-497 https://doi.org/10.2514/3.20862
  15. Renders, J. M. and Flasse, S. P., 1996, 'Hybrid Methods Using Genetic Algorithms for Global Optimization,' IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 26, No. 2 https://doi.org/10.1109/3477.485836
  16. Schlierkamp-Voosen, D. and Muhlenbein, H., 1996, 'Adaptation of Population Sizes by Competing Subpoplations,' IEEE International Conference on Evolutionary Computation, pp. 330-335
  17. Schwefel, H. P., 1995, 'Evolution Optimum Seeking,' Sixth Generation Computer Technology Series, Wiley
  18. Shi, Y., Eberhart, R. and Chen, Y., 1999, 'Implementation of Evolutionary Fuzzy Systems,' IEEE Transactions on Fuzzy Systems, Vol. 7, No. 2, pp. 109-119 https://doi.org/10.1109/91.755393
  19. Spears, W. M., 1995, Evolutionary Programming IV, The MIT Press, pp. 73-120
  20. Srinivas, M. and Patnaik, L. M, 1994, 'Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms,' IEEE Transactions on Systems, Man and Cybernetics, Vol. 24, No. 4, pp. 656-667 https://doi.org/10.1109/21.286385
  21. Won, T. H., Song, D. S. and Lee, M. H., 1999, 'A Design of Fuzzy Variable Structure Controller for F-18 Aircraft,' KSME, Vol. 23, No. 12, pp. 2109-2119
  22. Zhou, Y. S. and Lai, L. Y., 2000, 'Optimal Design for Fuzzy Controllers by Genetic Algorithms,' IEEE Transactions on Industry Applications, Vol. 36, pp. 93-97 https://doi.org/10.1109/28.821802