Neural Networks for Air Data Estimation: Test of Neural Network Simulating Real Flight Instruments

  • Manuela Battipede
  • Piero Gili
  • Angelo Lerro
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)


In this paper virtual air data sensors have been modeled using neural networks in order to estimate the aircraft angles of attack and sideslip. These virtual sensors have been designed and tested using the aircraft mathematical model of the De Havilland DHC-2. The aim of the work is to evaluate the degradation of neural network performance, which is supposed to occur when real flight instruments are used instead of simulated ones. The external environment has been simulated, and special attention has been devoted to electronic noise that affects each input signals examining modern devices.. Neural networks, trained with noise free signals, demonstrate satisfactory agreement between theoretical and estimated angles of attack and sideslip.


Neural network turbulence noise air data 


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  1. 1.
    Sakamoto, G.M.: Aerodynamic Characteristics of a Vane Flow Angularity Sensor System Capable of Measuring Flightpath Accelerations for the Mach Number Range from 0.40 to 2.54. NASA-TN-D-8242, p. 45 (1976)Google Scholar
  2. 2.
    Calia, A., et al.: Multi-hole probe and elaboration algorithms for the reconstruction of the air data parameters. In: IEEE International Symposium on Industrial Electronics, ISIE 2008 (2008)Google Scholar
  3. 3.
  4. 4.
    Napolitano, M.R., An, Y., Seanor, B.A.: A fault tolerant flight control system for sensor and actuator failures using neural networks. Aircraft Design 3(2), 103–128 (2000)CrossRefGoogle Scholar
  5. 5.
    Oosterom, M., Babuska, R.: Virtual sensor for fault detection and isolation in flight control systems - fuzzy modeling approach. In: Proceedings of the 39th IEEE Conference on Decision and Control (2000)Google Scholar
  6. 6.
    Tomayko, J.E.: Computers Take Flight: A History of NASA’s Pioneering Digital Fly-By-Wire Project. NASA 2000. SP-2000-4224 (2000) Google Scholar
  7. 7.
    Norgaard, M., Jorgensen, C.C., Ross, J.C.: Neural Network Prediction of New Aircraft Design Coefficients. NASA Technical Memorandum (112197) (1997) Google Scholar
  8. 8.
    Oosterom, M., Babuska, R.: Virtual Sensor for the Angle-of-Attack Signal in Small Commercial Aircraft. In: 2006 IEEE International Conference on Fuzzy Systems (2006)Google Scholar
  9. 9.
    Samara, P.A., Fouskitakis, G.N., Sakellariou, J.S., Fassois, S.D.: Aircraft Angle-Of-Attack Virtual Sensor Design via a Functional Pooling Narx Methodology. In: Proceedings of the European Control Conference (ECC), Cambridge, UK (2003)Google Scholar
  10. 10.
    Xiaoping, D., et al.: A prediction model for vehicle sideslip angle based on neural network. In: 2010 2nd IEEE International Conference on Information and Financial Engineering (ICIFE), pp. 451–455 (2010)Google Scholar
  11. 11.
    Rohloff, T.J., Whitmore, S.A., Catton, I.: Air Data Sensing from Surface Pressure Measurements Using a Neural Network Method. AIAA Journal 36(11)Google Scholar
  12. 12.
    Samy, I., Postlethwaite, I., Green, J.: Neural-Network-Based Flush Air Data Sensing System Demonstrated on a Mini Air Vehicle. Journal of Aircraft 47(1) Google Scholar
  13. 13.
    McCool, K., Haas, D.: Neural network system for estimation of aircraft flight data Google Scholar
  14. 14.
    di Fusco, C.: Ricostruzione degli Angoli Di Incidenza e di Derapata del Velivolo Mediante Elaborazioni dei Dati Aria Basate su Reti Neurali, Department of Aerospace Engineering. University of Pisa, Pisa (2006) Google Scholar
  15. 15.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)CrossRefGoogle Scholar
  16. 16.
    Svobodova, J., Koudelka, V., Raida, Z.: Aircraft equipment modeling using neural networks. In: 2011 International Conference on Electromagnetics in Advanced Applications (ICEAA) (2011) Google Scholar
  17. 17.
    Rauw, M.: FDC 1.2 – A Simulink Toolbox for Flight Dynamics and Control Analysis (2001) Google Scholar
  18. 18.
    Etkin, B.: Dynamics of flight: stability and control. Wiley (1982)Google Scholar
  19. 19.
    Haering Jr., E.A.: Airdata Measurement and Calibration, N.A.a.S. Administration, Editor (1995) Google Scholar
  20. 20.
    Skapura, D.M.: Building Neural Networks. ACM Press (1996)Google Scholar
  21. 21.
    Gladiator Technologies, I.: High Performance MEMS AHRS “LN Series” (2011) Google Scholar
  22. 22.
    Sensortec, B.: BMP085 Digital Pressure Transducer (2011)Google Scholar
  23. 23.
    Honeywell Model AS25D Aerospace Pressure Transducers Google Scholar
  24. 24.
    DEFENSE, D.O.: Environmental Engineering Considerations and Laboratory Tests, MIL-STD-810E Google Scholar
  25. 25.
    Norgaard, M.: The nnsysid toolbox for use with Matlab (2003),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Manuela Battipede
    • 1
  • Piero Gili
    • 1
  • Angelo Lerro
    • 1
  1. 1.Department of Mechanical and Aerospace EngineeringPolitecnico di TorinoTorinoItaly

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