Gain-scheduled directional guidance controller design using a genetic algorithm for automatic precision landing

  • Cheolkeun HaEmail author
Regular Papers Intelligent and Information Systems


This paper discusses the guidance controller design problem of an aircraft in automatic landing and touchdown flight, subject to dangerous and unpredictable gusts known as wind-shear and to directional crosswind. The associated airplane in the landing flight was statically unstable in this paper. The wind-shear, based on the Dryden gust model, was included in the nonlinear airplane model. A directional guidance control system with gain-scheduling fuzzy logic was proposed in this paper. In fuzzy logic, an even number of exponential membership functions in the output are considered and their shape, decay rate, and scaling factors are optimized using a genetic algorithm. In this control system, the glide slope capture logic and the flare logic were also included for longitudinal and lateral control, respectively. The nonlinear aircraft model simulation illustrated that the proposed guidance control system shows satisfactory performances in accurate touchdowns and is adequately robust to the strong crosswind and wind-shear turbulences.


Automatic landing fuzzy gain-scheduled controller genetic algorithm wind shear 


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  1. [1]
    National Transportation Safety Board Weather Related Accidents,
  2. [2]
    R. E. Bach and R. C. Wingrove, “The analysis of airline flight records for winds and performance with application to the delta 191 accident,” Proc. of the AIAA Atmospheric Flight Mechanics Conference, pp. 361–373, Aug. 18–20, 1986.Google Scholar
  3. [3]
    J. Shen, E. K. Park, and R. E. Bach, “Comprehensive analysis of two downburst-related aircraft accidents,” Journal of Aircraft, vol. 33, no. 5, pp. 924–930, 1996.CrossRefGoogle Scholar
  4. [4]
    M. L. Psiaki and R. F. Stengel, “Analysis of aircraft control strategies for microburst encounter,” Journal of Guidance, Control, and Dynamics, vol. 8, no. 5, pp. 553–559, Sept.–Oct., 1985.CrossRefGoogle Scholar
  5. [5]
    W. Frost, Flight in Low Level Wind Shear, NASA CR-3678, 1983.Google Scholar
  6. [6]
    National Severe Storms Laboratory, URL: (cited on July 13, 2005).
  7. [7]
    B. L. Stevens and F. L. Lewis, Aircraft Control and Simulation, John Wiley & Sons, Inc., New York, 1992.Google Scholar
  8. [8]
    M. B. Ghalia and A. T. Alouani, “Robust control design of an auto-landing system,” Proc. of the 25th Southeastern Symposium on System Theory, pp. 248–252, March 1993.Google Scholar
  9. [9]
    R. J. Niewoehner and I. I. Kaminer, “Design of an auto-landing controller for an F-14 aircraft using H — synthesis,” Journal of Guidance, Control, and Dynamics, vol. 19, no. 3, pp. 656–663, 1966.CrossRefGoogle Scholar
  10. [10]
    M. B. Subrahmanyam, “H design of F/A-18A automatic carrier landing system,” Journal of Guidance, Control, and Dynamics, vol. 17, no. 1, pp. 187–191, 1994.CrossRefGoogle Scholar
  11. [11]
    S. P. Shue and R. K. Agrawal, “Design of automatic landing using mixed H 2 / H control,” Journal of Guidance, Control, and Dynamics, vol. 22, no. 1, pp. 103–114, Jan.–Feb. 1999.CrossRefGoogle Scholar
  12. [12]
    A. P. Kurdjukov, B. V. Pavlov, and V. N. Timin, “Longitudinal flight control in windshear via H — methods,” Proc. of AIAA Guidance, Navigation, and Control Conference, AIAA 96-3727, pp. 1–6, 1996.Google Scholar
  13. [13]
    S. P. Shue, K. Ramesh, and P. Shi, “Robust aircraft control design for glide slope capture in windshear using gain scheduling,” Proc. of AIAA Guidance, Navigation, and Control Conference, AIAA 98-4299, pp. 1–12, 1998.Google Scholar
  14. [14]
    Y. Zhao and A. E. Bryson, “Optimal paths through downburst,” J. of Guidance, Control and Dynamics, vol. 13, no. 5, pp. 813–818, Sept.–Oct. 1985.CrossRefGoogle Scholar
  15. [15]
    Y. Zhao and A. E. Bryson, “Control of an aircraft in downburst,” J. of Guidance, Control and Dynamics, vol. 13, no. 5, pp. 819–823, Sept.–Oct. 1985.CrossRefGoogle Scholar
  16. [16]
    J. Juang, H. Chang, and K. Cheng, “Intelligent landing control using linearized inverse aircraft model,” Proc. of American Control Conference, vol. 4, pp. 3269–3274, 2001.Google Scholar
  17. [17]
    K. Nho and R. K. Agarwal, “Automatic landing system design using fuzzy logic,” J. of Guidance, Control and Dynamics, vol. 23, no. 2, pp. 298–304, Mar.–Apr. 2000.CrossRefGoogle Scholar
  18. [18]
    J. Juang, K. Chin, and J. Chio, “Intelligent automatic landing system using fuzzy neural networks and genetic algorithm,” Proc. of American Control Conference, Boston, MA, pp. 5790–5795, June 30–July 2, 2004.Google Scholar
  19. [19]
    C. Ha and J. Kim, “Automatic landing under wind shear turbulence in adaptive gain scheduled PID control optimized in genetic algorithm,” Proc. of AIAA Guidance, Navigation and Control Conference and Exhibit, AIAA 2005-6346, 15–18 August 2005.Google Scholar
  20. [20]
    Z. Y. Zhao, M. Tomizuka, and S. Isaka, “Fuzzy gain scheduling of PID controllers,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 23, no. 5, pp. 1392–1398, Sept.–Oct. 1993.CrossRefGoogle Scholar
  21. [21]
    D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.Google Scholar
  22. [22]
    Y. J. Park, H. S. Cho, and D. H. Cha, “Genetic algorithm-based optimization of fuzzy logic controller using characteristic parameters,” Proc. of IEEE International Conference on Evolutionary Computation, pp. 831–836, 1995.Google Scholar
  23. [23]
    D. McLean, Automatic Flight Control Systems, Prentice Hall, 1990.Google Scholar
  24. [24]
    R. A. Nichols, R. T. Reichert, and J. H. Evers, “Gain scheduling for controllers: a flight control examples,” IEEE Trans. on Control Systems Technology, vol. 1, no. 2, pp. 69–79, 1993.CrossRefGoogle Scholar
  25. [25]
    K. C. Jeong, S. H. Kwon, D. H. Lee, M. W. Lee, and J. Y. Choi, “A fuzzy logic-based gain tuner for PID controllers,” Proc. of IEEE World Congress on Computational Intelligence, pp. 551–554, 4–6 May, 1998.Google Scholar
  26. [26]
    S. Hayashi, “Auto tuning fuzzy logic controller,” Proc. of IFSA International Fuzzy Systems Associates, pp. 41–44, 1991.Google Scholar
  27. [27]
    L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.zbMATHCrossRefMathSciNetGoogle Scholar
  28. [28]
    D. Whitely, A Genetic Algorithm Tutorial, Technical Report CS-93-103, Colorado State University, 1993.Google Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Aerospace EngineeringUniversity of UlsanUlsanKorea

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