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162             4. NEURAL NETWORK BLACK BOX MODELING OF AIRCRAFT CONTROLLED MOTION

                                              ◦
                         tack lies now within ±2 , and in some cases even  [10] Roskam J. Airplane flight dynamics and automatic
                                  ◦
                         within ±4 .                                      flight control. Part I. Lawrence, KS: DAR Corporation;
                            Thus, the adaptation mechanisms allow to re-  1995.
                         duce the tracking error in a much larger region  [11] Roskam J. Airplane flight dynamics and automatic
                                                                          flight control. Part II. Lawrence, KS: DAR Corporation;
                         of state space (in the case of a nonlinear system)  1998.
                         and to expand the frequency band in which the  [12] Cook MV. Flight dynamics principles. Amsterdam: El-
                         tracking error does not exceed a specific prede-  sevier; 2007.
                         termined value. That is, endowing the system  [13] Hull DG. Fundamentals of airplane flight mechanics.
                         with adaptive properties allows it to deal with a  Berlin: Springer; 2007.
                                                                      [14] Nguyen LT, Ogburn ME, Gilbert WP, Kibler KS,
                         much broader class of parametric uncertainties
                                                                          Brown PW, Deal PL. Simulator study of stall/post-stall
                         in controlled objects.                           characteristics of a fighter airplane with relaxed longi-
                            We can draw some conclusions from the re-     tudinal static stability. NASA TP-1538, Dec. 1979.
                         sults obtained in Section 4.3.4.The methodsof  [15] Sonneveld L. Nonlinear F-16 model description. The
                         adaptive-robust modeling and control in vari-    Netherlands: Control & Simulation Division, Delft Uni-
                                                                          versity of Technology; June 2006.
                         ants with a reference and predictive model
                                                                      [16] Shaughnessy JD, Pinckney SZ, et al. Hypersonic vehicle
                         are the powerful and promising tools that al-    simulation model: Winged-cone configuration. NASA–
                         low solving the problems of fault-tolerant air-  TM–102610, November 1990.
                         craft motion control under uncertainty condi-  [17] Boyden RP, Dress DA, Fox CH. Subsonic static and
                         tions.                                           dynamic stability characteristics of the test technique
                                                                          demonstrator NASP configuration. In: 31st Aerospace
                                                                          Sciences Meeting & Exhibit, January 11–14, 1993, Reno,
                                                                          NV, AIAA-93-0519.
                                       REFERENCES                     [18] Boyden RP, Dress DA, Fox CH, Huffman JK, Cruz CI.
                                                                          Subsonic static and dynamic stability characteristics of
                          [1] Ljung L. System identification: Theory for the user. 2nd  a NASP configuration. J Aircr 1994;31(4):879–85.
                             ed. Upper Saddle River, NJ: Prentice Hall; 1999.  [19] Davidson J. et al., Flight control laws for NASA’s Hyper-
                          [2] Narendra KS, Parthasarathy K. Identification and con-  X research vehicle. AIAA–99–4124.
                             trol of dynamic systems using neural networks. IEEE  [20] Engelund WC. Holland SD, et al., Propulsion system
                             Trans Neural Netw 1990;1(1):4–27.            airframe integration issues and aerodynamic database
                          [3] Chen S, Billings SA. Neural networks for nonlinear dy-  development for the Hyper-X flight research vehicle.
                             namic systems modelling and identification. Int J Con-  ISOABE–99–7215.
                             trol 1992;56(2):319–46.                  [21] Engelund WC, Holland SD, Cockrell CE, Bittner RD,
                          [4] Heister F, Müller R. An approach for the identification  Aerodynamic database development for the Hyper-X
                             of nonlinear, dynamic processes with Kalman-filter-  airframe integrated scramjet propulsion experiments.
                             trained recurrent neural structures. Research report se-  In: AIAA 18th Applied Aerodynamics Conference, Au-
                             ries, Report No. 193, University of Würzburg, Institute  gust 14–17, 2000, Denver, Colorado. AIAA 2000–4006.
                             of Computer Science; April 1999.         [22] Holland SD, Woods WC, Engelund WC. Hyper-X re-
                          [5] Haykin S. Neural networks: A comprehensive founda-  search vehicle experimental aerodynamics test program
                             tion. 2nd ed. Upper Saddle River, NJ, USA: Prentice  overview. J Spacecr Rockets 2001;38(6):828–35.
                             Hall; 1998.                              [23] Morelli, E.A. Derry, S.D. Smith, M.S. Aerodynamic pa-
                          [6] Hagan MT, Demuth HB, Beale MH, De Jesús O. Neural  rameter estimation for the X-43A (Hyper-X) from flight
                             network design. 2nd ed. PSW Publishing Co.; 2014.  data. In: AIAA Atmospheric Flight Mechanics Confer-
                          [7] Gorban AN. Generalized approximation theorem and  ence and Exhibit, August 15–18, 2005. San Francisco,
                             computational capabilities of neural networks. Sib J Nu-  CA. AIAA 2005-5921.
                             mer Math 1998;1(1):11–24 (in Russian).   [24] Davis MC, White JT. X-43A flight-test-determined aero-
                          [8] Etkin B, Reid LD. Dynamics of flight: Stability and con-  dynamic force and moment characteristics at Mach 7.0.
                             trol. 3rd ed. New York, NY: John Wiley & Sons, Inc.;  J Spacecr Rockets 2008;45(3):472–84.
                             2003.                                    [25] Morelli EA. Flight test experiment design for character-
                          [9] Boiffier JL. The dynamics of flight: The equations.  izing stability and control of hypersonic vehicles. J Guid
                             Chichester, England: John Wiley & Sons; 1998.  Control Dyn 2009;32(3):949–59.
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