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Application of neural networks for the design of flight control algorithms. II Adaptive tuning of neural network control law

  • Flight Dynamics and Control of Flight Vehicles
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Abstract

In this paper, we consider different approaches for the neural network controller tuning in the flight control system. Two of the most common tuning approaches in the adaptive control theory are applied. The first one uses parameter identification technique and consists in solving a real-time regression problem for the control law. The second approach is based on the Lyapunov direct method, which utilizes a tracking error as an absolute measure of tuning performance. The neural network control law are designed for the three-axis flight control problem and tested on the full nonlinear model of a fighter aircraft. Closed loop simulation results are presented and two adaptation algorithms are compared in the case of abrupt change of aircraft dynamics.

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References

  1. Aerodinamika, ustoichivost’ i upravlyemost’ sverkhzvukovykhh samoletov (Aerodynamics, Stability and Controllability of Supersonic Aircraft), Byushengens, G.S., (Ed.), Moscow: Nauka, Fizmatlit, 1998.

    Google Scholar 

  2. Sonneveldt, L., Nonlinear F-16 Fighter Model, Matlab Central — An Open Exchange for the MATLAB and Simulink User Community, URL: http://www.mathworks.com/matlabcentral.

  3. Sastry, S. and Bodson, M., Adaptive Control: Stability, Convergence, and Robustness, Englewood Cliffs, New Jersey: Prentice Hall, 1989.

    MATH  Google Scholar 

  4. Miroshnik, I.V., Nikiforov, V.O., and Fradkov, A.L., Nelineinoe i adaptivnoe upravlenie slozhnymi dinamicheskimi sistemami (Nonlinear and Adaptive Control of Complex Dynamic Systems), St. Petersburg: Nauka, 2000.

    Google Scholar 

  5. Astrom, K. J. and Wittenmark, B., Adaptive Control, New Jersey: Prentice Hall, 1994.

    Google Scholar 

  6. Ioannou, P. A. and Sun, J., Robust Adaptive Control, New Jersey: Prentice Hall, 1995.

    Google Scholar 

  7. Farrell, J. A. and Polycarpou, M.M., Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, N.Y.: John Wiley & Sons, 2006.

    Book  Google Scholar 

  8. Spooner, J. T., Maggiore, M., Ordonez, R., and Passino, K.M., Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques, N.Y.: John Wiley & Sons, 2002.

    Book  Google Scholar 

  9. Terekhov, V.A., Efimov, D.V., and Tyukin, I.Yu., Neirosetevye sistemy upravleniya (Neural Network Control Systems), Moscow: IPRZHR, 2002.

    Google Scholar 

  10. Kim, B.S. and Calise, A.J., Nonlinear Flight Control Using Neural Networks, AIAA Guidance, Navigation, and Control Conference, Scottsdale, Arizona, 1994.

    Google Scholar 

  11. Nakanishi, J. and Schaal, S., Feedback Error Learning and Nonlinear Adaptive Control, Neural Networks, 2004, no. 17, pp. 1453–1465.

    Google Scholar 

  12. Sonneveldt, L., Van Oort, E. R., Chu, Q. P., de Visser, C.C., and Mulder, J. A., Lyapunov-based Fault Tolerant Flight Control Designs for a Modern Fighter Aircraft Model, AIAA Guidance, Navigation, and Control Conference, Chicago, Illinois, 2009.

    Google Scholar 

  13. Burken, John J., Nguyen, Nhan T., and Griffin, Brian J., Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model, AIAA 2019-3364, NASA Dryden Flight Research Center, 2010.

    Google Scholar 

  14. Neural Systems for Control, Omidvar, O.M. and Elliott. D.L., Academic Press, 1997. 358 p.

    Google Scholar 

  15. Haykin, Simon S., Neural Networks, Prentice Hall Int., 2006.

    Google Scholar 

  16. Narendra, K.S. and Parthasarathy, K., Identification and Control of Dynamic Systems Using Neural Networks, IEEE Trans. on Neural Networks, 1990, vol. 1, no. 1, pp. 4–27.

    Article  Google Scholar 

  17. Hagan, M.T., De Jesus O, and Schultz, R., Training Recurrent Networks for Filtering and Control, Recurrent Neural Networks: Design and Applications. USA: CRC Press, 1999. Chapter 12. P. 311–340.

    Google Scholar 

  18. Psaltis, D., Sideris, A., and Yamamura, A.A., A Multilayered Neural Network Controller, IEEE Control Systems Magazine, 1988, vol. 8, no. 2, pp. 17–21.

    Article  Google Scholar 

  19. Spravochnik po teorii avtomaticheskogo upravleniya (Handbook on Automatic Control Theory), Krasovskii, A.A., Ed., Moscow: Nauka, 1987.

    Google Scholar 

  20. Tsypkin, Ya.Z., Adaptatsiya i obuchenie v avtomaticheskikh sistemakh (Adaptation and Training in Automatic Controls), Moscow: Nauka, 1968.

    Google Scholar 

  21. Kondrat’ev, A.I. and Tyumentsev, Yu.V., Neural Network Adaptive Fail-Safe Control of Maneuverable Aircraft Motion, Trudy vsepossiiskoi nauchno-tekhnischeskoi konferentsii “Neiroinformatika-2010” (Proc. All-Russian Sc.-Tech. Conf. “Neural Automatics-2010”, Moscow: Izd. MIFI, 2010.

    Google Scholar 

  22. Kalman Filtering and Neural Networks, Haykin, S., Ed., N.Y.: John Wiley & Sons, 2001.

    Google Scholar 

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Original Russian Text © A.I. Kondrat’ev, Yu.V. Tyumentsov, 2013, published in Izvestiya VUZ. Aviatsionnaya Tekhnika, 2013, No. 3, pp. 34–39.

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Kondrat’ev, A.I., Tyumentsev, Y.V. Application of neural networks for the design of flight control algorithms. II Adaptive tuning of neural network control law. Russ. Aeronaut. 56, 257–265 (2013). https://doi.org/10.3103/S1068799813030070

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  • DOI: https://doi.org/10.3103/S1068799813030070

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