Advertisement

Aerotecnica Missili & Spazio

, Volume 93, Issue 3–4, pp 83–92 | Cite as

Wind Shear On-Line Identification for Unmanned Aerial Systems

  • C. Grillo
  • F. Montano
  • M. Patti
Article

Abstract

An algorithm to perform the on line identification of the wind shear components suitable for the UAS characteristics has been implemented. The mathematical model of aircraft and wind shear in the augmented state space has been built without any restrictive assumption on the dynamic of wind shear. Due to the severe accelerations on the aircraft induced by the strong velocity variation typical of wind shear, the wind shear effects have been modeled as external forces and moments applied on the aircraft. The identification problem addressed in this work has been solved by using the Filter error method approach. An Extended Kalman Filter has been developed to propagate state. It has been tuned by using a database of measurements through off-line identification of the process noise covariance matrix. Afterwards the implemented EKF has been employed to estimate onboard either aircraft state or turbulence, with significant savings in terms of time and computing resources. Robustness of implemented algorithm has been verified by means of several tests. The obtained results show the feasibility of the tuned up algorithm. In fact it is possible, by using a few numbers of low cost sensors, to estimate with a noticeable accuracy the augmented state vector. Besides a very short computation time is required to perform the augmented state estimation even by using low computation power.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    E. Fedorovich, R. Conzemius, “Effects of wind shear on the atmospheric convective boundary layer structure and evolution”, Acta Geophysica, Vol. 56, No. 1, pp. 114–141 (2008).CrossRefGoogle Scholar
  2. 2.
    S. S. Mulgund, R. F. Stengel, “Optimal Non linear Estimation for Aircraft Flight Control in Wind Shear”, Automatica, Vol. 32 No. 1, pp.3–13 (1996).MathSciNetCrossRefGoogle Scholar
  3. 3.
    W. Frost, R. L. Bowles, “Wind shear terms in the equations of aircraft motion”, Journal of Aircraft, Vol. 21, No. 11, pp. 866–872 (1984).CrossRefGoogle Scholar
  4. 4.
    R. F. Stengel, “Course Notes for MAE 566”, Aircraft Dynamics, Princeton University. (1990).Google Scholar
  5. 5.
    R. M. Oseguera, R. L. Bowles, “A simple, analytic 3-dimensional downburst model based on boundary layer stagnation flow”, NASA TM 100632, (1988).Google Scholar
  6. 6.
    P. Baldi, P. Castaldi, N. Mimmo, A. Torre and S. Simani, “A New Longitudinal Flight Path Control with Adaptive Wind Shear Estimation and Compensation”, 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), Orlando, FL, USA, (2011).CrossRefGoogle Scholar
  7. 7.
    D. Mc Lean, “Automatic Flight Control Systems”, Prentice Hall Int., London, (1990).Google Scholar
  8. 8.
    C. Grillo, F.P. Vitrano, “State Estimation of a Nonlinear Unmanned Aerial Vehicle Model Using an Extended Kalman Filter”, 15th AIAA International Space Planes and Hypersonic Systems and Technologies Conference, Dayton, Ohio (2008).CrossRefGoogle Scholar
  9. 9.
    B. Etkin, “Dynamics of Atmospheric Flight”, Dover Publications, (2005)Google Scholar
  10. 10.
    V. Ravindra Jategaonkar, “Flight Vehicle System Identification — A Time Domain Methodology”, American Institute of Aeronautics and Astronautics, Vol. 216, (2009).Google Scholar

Copyright information

© AIDAA Associazione Italiana di Aeronautica e Astronautica 2014

Authors and Affiliations

  • C. Grillo
    • 1
  • F. Montano
    • 1
  • M. Patti
    • 1
  1. 1.Dipartimento di Ingegneria Chimica, Gestionale, Informatica, MeccanicaUniversità di PalermoItaly

Personalised recommendations