Using RISE Observer to Implement Patchy Neural Network for the Identification of “Wing Rock” Phenomenon on Slender Delta 80° Wings

  • Paraskevas M. Chavatzopoulos
  • Thomas Giotis
  • Manolis Christodoulou
  • Haris Psillakis
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)


In this paper, the “wing rock” phenomenon is described for slender delta 80° wing aircrafts on the roll axis. This phenomenon causes the aircraft to undergo a strong oscillatory movement with amplitude dependent on the angle of attack. The objective is to identify “wing rock” using the Patchy Neural Network (PNN), which is a new form of neural nets. For the update of the weights of the network, an observer called RISE (Robust Integral of Sign Error) and equations of algebraic form are used. This causes the PNN to be fast, efficient and of a low computational cost.


wing rock phenomenon RISE observer Patchy Neural Network (PNN) slender delta 80° wing aircrafts 


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  1. 1.
    Guglieri, G., Quagliotti, F.B.: Analytical and experimental analysis of wing rock. Nonlinear Dynamics 24, 129–146 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Elzebda, J.M., Nayfeh, A.H., Mook, D.T.: Development of an analytical model of wing rock for slender delta wings. Journal of Aircraft 26(8), 737–743 (1989)CrossRefGoogle Scholar
  3. 3.
    Guglieri, G., Quagliotti, F.: Experimental observation of the wing rock phenomenon. Aerospace Science and Tecnology, 111–123 (1997)Google Scholar
  4. 4.
    Gurney, K.: An Introduction to neural network, p. 234. ULC Press (1997)Google Scholar
  5. 5.
    Abbasi, N.: Small note on using Matlab ode45 to solve differential equations (August 2006)Google Scholar
  6. 6.
    Slender Wing Theory,
  7. 7.
    MATLAB, The Language of Technical Computing. Getting Started with MATLAB. Version 5. The Mathworks (December 1996) Google Scholar
  8. 8.
    Psillakis, H.E., Christodoulou, M.A., Giotis, T., Boutalis, Y.: An observer approach for deterministic learning with patchy neural network. International Journal of Artificial Life Research 2(1), 1–16 (2011)CrossRefGoogle Scholar
  9. 9.
    Wang, C., Hill, D.J.: Deterministic learning theory for identification, recognition and control, 1st edn., p. 207. CRC Press (2009)Google Scholar
  10. 10.
    Fonda, J.W., Jagannathan, S., Watkins, S.E.: Robust neural network RISE observer based fault diagnostics and prediction. In: Proc. of the IEEE Internation Conference on Neural Networks (2010)Google Scholar
  11. 11.
    Nelson, R.C., Pelletier, A.: The unsteady aerodynamics of slender wings and aircraft undergoing large amplitude maneuvers. Progress in Aerospace Sciences 39, 185–248 (2003)CrossRefGoogle Scholar
  12. 12.
    Paraskevas-Marios, C.: Use of observer, based on neural networks, for identification of the “wing rock” phenomenon on delta 80 degrees wing aircrafts, p. 130. Technical University of Crete, Dept of Electronic and Computer Engineers, Chania (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paraskevas M. Chavatzopoulos
    • 1
  • Thomas Giotis
    • 1
  • Manolis Christodoulou
    • 1
  • Haris Psillakis
    • 2
  1. 1.Department of Electronic and Computer EngineeringTechnical University of CreteChaniaGreece
  2. 2.Department of Electrical EngineeringTechnological & Educational Institute of CreteHeraklionGreece

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