Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2003.9.4.329

Reconfigurable Flight Control Law Using Adaptive Neural Networks and Backstepping Technique  

신동호 (서울대학교 항공우주공학과)
김유단 (서울대학교 항공우주공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.9, no.4, 2003 , pp. 329-339 More about this Journal
Abstract
A neural network based adaptive controller design method is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness decrease caused by control surface damage. The neural network based adaptive nonlinear controller is developed by making use of the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. On-line teaming neural networks are implemented to guarantee reconfigurability and robustness to the uncertainties caused by aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed with assumption that not any of the nonlinear functions of the system is known accurately, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks loam through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated according to the Lyapunov theory. A nonlinear dynamic model of an F-16 aircraft is used to demonstrate the effectiveness of the proposed control law.
Keywords
neural networks; backstepping; reconfigurable flight control; control surface damage;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Nonlinear Control Law with Application to High Angle-of-Attack Flight /
[ D. J. Bugajski;D. F. Enns ] / Journal of Guidance, Control, and Dynamics   DOI
2 S. A. Snell, D. F. Enns and W. L. Garrard, 'Nonlinear Control of a Supermaneuverable Aircraft,' Proceedings of the AIAA Guidance, Navigation, and Control Conference, AIAA Paper 89-3486, Washington, DC, 1989
3 P. Menon, M. Badgett and R. Walker, 'Nonlinear Flight Test Trajectory Controllers for Aircraft,' Journal of Guidance, Control, and Dynamics, vol. 10, no. 1,1987, pp. 67-72   DOI   ScienceOn
4 S. A. Snell, D. F. Enns and W. L. Garrard, 'Nonlinear Inversion Flight Control for a Supermaneuverable Aircraft,' Journal of Guidance, Control, and Dynamics, vol. 15, no. 4, 1992, pp. 976-984   DOI
5 W. D. Morse and K. A. Ossman, 'Model Following Reconfigurable Flight Control System for the AFT/F16,' Journal of Guidance, Control, and Dynamics, vol. 13, no. 6, 1990, pp. 969-976   DOI
6 Y. Ochi and K. Kanai, 'Design of Restructurable Flight Control Systems Using Feedback Linearization,' Journal of Guidance, Control, and Dynamics, vol. 14, no. 5,1991, pp. 903-911   DOI
7 Y. Shtessel, J. Buffington and S. Banda 'Multiple Timescale Flight Control Using Reconfigurable Sliding Modes,' Journal of Guidance, Control, and Dynamics, vol. 22, no. 6, 1999, pp. 873-883   DOI
8 K. Homik, M. Stinchcombe and H. White, 'Multilayer Feedforward Networks are Universal Approximators,' Neural Networks, vol. 2, no. 5, 1989, pp. 359-366   DOI   ScienceOn
9 R. T. Rysdyk and A. J. Calise, 'Adaptive Model Inversion Flight Control for Tilt-Rotor Aircraft,' Journal of Guidance. Control, and Dynamics, vol. 22, no. 3, 1999, pp. 402-407   DOI   ScienceOn
10 B. S. Kim and A. J. Calise, 'Nonlinear Adaptive Flight Control Using Neural Networks,' Journal of Guidance, Control, and Dynamics, vol. 20, no. 1, 1997, pp. 26-33   DOI   ScienceOn
11 D. J. Bugajski and D. F. Enns, 'Nonlinear Control Law with Application to High Angle-of-Attack Flight,' Journal of Guidance, Control, and Dynamics, vol. 15, no. 3, 1992, pp. 761-767   DOI
12 A. Calise, S. Lee and M. Sharma, 'Development of a Reconfigurable Flight Control Law for Tailess Aircraft,' Journal of Guidance, Control, and Dynamics, vol. 24, no. 5, 2001, pp. 896-902   DOI   ScienceOn
13 T. Lee and Y. Kim, 'Nonlinear Adaptive Flight Control Using Backstepping and Neural Networks Controller,' Journal of Guidance, Control, and Dynamics, vol. 24, no. 4, 2001, pp. 675-682   DOI   ScienceOn
14 F. L. Lewis, A. J. Yesildirek and K. Lin, 'Multilayer Neural-Net Robot Controller with Guaranteed Tracking Performance,' IEEE Transaction on Neural Networks, vol.7, no. 2, 1996, pp. 388-399   DOI   ScienceOn
15 M. B. McFarland and A. J. Calise, 'Adaptive Nonlinear Control of Agile Antiair Missiles Using Neural Networks,' IEEE Transaction on Control Systems Technology, vol. 8, no. 5, 2000, pp.749-756   DOI   ScienceOn
16 P. Q. Ioannou and J. Sun, Robust Adaptive Control, Prentice Hall, New Jersey, 1996, Chap. 8
17 K. S. Narendra and A. M. Annaswamy, Stable Adaptive Systems, Prentice Hall, New Jersey, 1996. Chap. 8
18 J., Leitner, A. J. Calise and J. V. R. Prasad, 'Analysis of Adaptive Neural Networks for Helicopter Flight Controls,' Journal of Guidance, Control, and Dynamics, vol. 20, no. 5, 1997, pp. 972-979   DOI   ScienceOn
19 B. L. Stevens and F. L. Lewis, Aircraft Control and Simulation, John Wiley and Sons, New York, 1992, Chap. 2
20 S. S. Ge, C. C. Hang and T. Zhang, 'A Direct Approach to Adaptive Controller Design and Its Application to Inverted Pendulum Tracking,' Proceedings of the American Control Conference, Philadelphia, Pennsylvania, 1998, pp. 1043-1047   DOI
21 E. A Morelli, 'Global Nonlinear Parametric Modeling with Application to F-16 Aerodynamics,' Proceedings of the 1998 American Control Conference, IEEE Publications, Piscataway, NJ, 1998,pp. 997-1001   DOI