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Inclusion of the D-optimality in multisine manoeuvre design for aircraft parameter estimation

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Języki publikacji
EN
Abstrakty
EN
This paper is concerned with the designing of simultaneous flight control deflections for aircraft system identification. The elevator, ailerons and rudder are excited with harmonically related multisine signals. The optimal deflections are designed when there is no information about the stability and control derivatives and when this information is available. The inclusion of the system dynamics in the inputs design phase is done with the D-optimality criterion. Both sets of optimal flight surface deflections are used as excitations of a nonlinear aircraft model which is identified through the maximum likelihood estimation method. Parameters accuracy for those maneuvers (designed with and without a-priori knowledge) is presented and compared.
Rocznik
Strony
87--98
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
autor
  • Warsaw University of Technology, Institute of Theoretical and Applied Mechanics, Warsaw, Poland
Bibliografia
  • 1. Etkin B., 1972, Dynamics of Atmospheric Flight, John Wiley and Sons, New York, USA
  • 2. Hendzel Z., Trojnacki M., 2014, Neural network identifier of a four-wheeled mobile robot subject to heel slip, Journal of Automation, Mobile Robotics & Intelligent Systems, 8, 4, 24-30
  • 3. Hoe G., Owensand D.B., Denham C., 2012, Forced oscillation wind tunnel testing for FASER flight research aircraft, AIAA Atmospheric Flight Mechanics Conference, Minneapolis, USA, AIAA 2012-4645
  • 4. Hopkin H.R., 1970, A Scheme of Notation and Nomenclature for Aircraft Dynamics and Associated Aerodynamics, Aeronautical Research Council, Report 3562
  • 5. Jameson P., Cooke A., 2012, Developing real-time system identification for UAVs, UKACC International Conference on Control 2012, Cardiff, UK, 958-963
  • 6. Jategaonkar R.V., 2006, Flight Vehicle System Identification: A Time Domain Methodology, AIAA, Reston, USA
  • 7. Kay S.M., 1993, Fundamentals of Statistical Signal Process – Estimation Theory, Prentice Hall, Upper Saddle River
  • 8. Lichota P., Ohme P., 2014, Design and Analysis of new Multi Axis Input Manoeuvres for Aircraft Sys-ID, Deutsches Zentrum f¨ur Luft- und Raumfahrt (DLR), Report DLR-IB 111-2014/46
  • 9. Mader C.A., Martins J.R.R.A., 2011, Computation of aircraft stability derivatives using an automatic differentiation adjoint approach, AIAA Journal, 49, 12, 2737-2750
  • 10. Mitchel M., 1999, An Introduction to Genetic Algorithms, MIT Press, Cambridge, USA
  • 11. Morelli E.A., 2003, Multiple input design for real-time parameter estimation in the frequency domain, 13th IFAC Symposium on System Identification, Rotterdam, Netherlands, REG-360
  • 12. Morelli E.A., 2012, Efficient global aerodynamic modeling from flight data, 50th AIAA Aerospace Sciences Meeting, Nashville, USA, AIAA-2012-1050
  • 13. Raab C., 2006, A Versatile and Modular Architecture for Aircraft System Simulation and Test, Deutsches Zentrum f¨ur Luft- und Raumfahrt (DLR), Report DLR-IB 111-2006/22
  • 14. Rogowski K., Maroński R., 2015, CFD computation of the Savonius rotor, Journal of Theoretical and Applied Mechanics, 53, 1, 37-45
  • 15. Schroeder M., 1970, Synthesis of low-peak-factor signals and binary sequences with low autocorrelation, IEEE Transactions on Information Theory, 16, 1, 85-89
  • 16. Zadeh L.A., 1962, From circuit theory to control theory, Proceeding of the IRE, 50, 5, 856-865
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniajacą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-331caf6c-17d7-4385-b83d-98d7b9b35858
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