Fuzzy Logic Controllers for Aircraft Flight Control

  • Jia Luo
  • Edward Lan
Part of the International Series in Intelligent Technologies book series (ISIT, volume 3)


Fuzzy logic proportional-integral-differential (PID) controllers are developed to perform both stability augmentation and automatic flight control functions. It operates for controlling both longitudinal and lateral-directional motions for an example aircraft, the X-29. The controllers for pitch, roll and yaw control are generated by analyzing a mathematical model describing aircraft static and dynamic characteristics. Each set of fuzzy rules consists of coarse and fine rules. The coarse rules are designed to supply fast response with large control input; while the fine rules are designed to make fine adjustments to improve dynamic stability. Nonlinear numerical simulations are performed to verify the controller performance at the design and off-design conditions for angle of attack hold, bank angle hold and sideslip angle hold. The results indicate that fuzzy PID controllers can provide fast, well damped response to pilot commands and thus improve flight performance, and increase agility, and also are robust.


Fuzzy Controller Fuzzy Logic Controller Flight Control Negative Medium Negative Small 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Zadeh, L. A., ”Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,” IEEE Trans. Syst., Man, Cybern., Vol. SMC-3, No. 1, pp. 28–44, 1973.MathSciNetGoogle Scholar
  2. [2]
    Mamdani, E. H. and Assilian, S., ”An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller,” Int. J. Man-Machine Studies, Vol. 7, pp. 1–13, 1975.MATHCrossRefGoogle Scholar
  3. [3]
    Larkin, L. I., ”A Fuzzy Logic Controller for Aircraft Flight Control,” Industrial Applications of Fuzzy Control, edited by M. Sugeno, Elsevier Science Publishers B. V. (North-Holland), 1985.Google Scholar
  4. [4]
    Chiu, S., Chand, S., Moore, D., and Chaudhary, A., ”Fuzzy Logic for Control of Roll and Moment for a Flexible Wing Aircraft,” IEEE Control Systems Magazine, June 1991, pp.42–48.Google Scholar
  5. [5]
    Rao, S. S. and Dhingra, A. K., ”Applications of Fuzzy Theories to Multi-Objective System Optimization,” NASA CR-177573, Jan. 1991.Google Scholar
  6. [6]
    Villarreal, J. A., Lea, R. N., and Savely, R. T., ”Fuzzy Logic and Neural Network Technologies,” AIAA Paper 92-0868, Jan. 1992.Google Scholar
  7. [7]
    Roskam, J., Airplane Flight Dynamics and Automatic Flight Controls, Part II, Roskam Aviation and Engineering Corporation, Ottawa, KS, 1982.Google Scholar
  8. [8]
    Valasek, J., Eggold, D., and Downing, D., ”A Study of a Proposed Modified Torsional Agility Metric,” AIAA Paper 91-2883-CP, August 1991.Google Scholar
  9. [9]
    Bernard, J. A., ”Use of a Rule-Based System for Process Control,” IEEE Control Systems Magazine, Oct. 1988, p.3.Google Scholar
  10. [10]
    Peng, X., Liu, S., Yamakawa, T., Wang, P., and Liu, X., ”Self-regulating PID Controller and its Applications to a Temperature Controlling Process,” Fuzzy Computing-Theory, Hardware, and Applications, edited by Gupta, M., and Yamakawa, T., Elsevier Science Publishers, 1988, pp.355–364.Google Scholar
  11. [11]
    Linse, D., ”Design and Analysis of a High Angle of Attack Flight Controls System,” MS thesis, the University of Kansas, 1987.Google Scholar
  12. [12]
    Luo, J., ”Aircraft Control Based on Fuzzy Logic,” Ph.D Dissertation, the University of Kansas, 1994.Google Scholar
  13. [13]
    Luo, J., and Lan, C. Edward, ”Development and Performance of a Fuzzy Logic Lateral Controller for X-29 Aircraft,” International Fuzzy Systems and Intelligent Control Conference, March 1993, Louisville, KY.Google Scholar
  14. [14]
    Procyk, T., and Mamdani, E., ”A Linguistic Self-Organizing Process Controller,” Automatica, Vol. 15, 1979, pp. 15–30.MATHCrossRefGoogle Scholar
  15. [15]
    Bosworth, John T., ”Linearized Aerodynamic and Control Law Models of the X-29A airplane and Comparison with Flight Data,” NASA Technical Memorandum 4356, February 1992.Google Scholar
  16. [16]
    Suikat, R., ”An Optimal Pole Placement Gain Scheduling Algorithm Using Output Feedback,” Ph.D Dissertation, the University of Kansas, 1987.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Jia Luo
    • 1
  • Edward Lan
    • 1
  1. 1.Department of Aerospace EngineeringUniversity of KansasLawrenceUSA

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