Advertisement

Search for equilibrium state flight

  • Jaroslav TupyEmail author
  • Ivan Zelinka
Conference paper

Abstract

In this paper, the calculation of aircraft steady state flight optima is discussed using a unique combination of global optimization theory and the direct computer simulation. The methods of artificial intelligence and heuristic algorithms handling with the equations of motion are presented in this paper. The main aim was to apply them in actuating the flying airplane into equilibrium state upon setting the control elements of various kinds to the needed position. New approach of SOMA (Self Organizing Migrating Algorithm) and DE (Differential Evolution) has been used here to find the vehicle stable state. The method can be practically utilized in construction of airplanes, unmanned aircraft as well as the flight simulators design.

Keywords

Evolutionary Algorithm Differential Evolution Unmanned Aircraft Aircraft Model Aircraft Flight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Lampinen Jouni, Zelinka Ivan, New Ideas in Optimization – Mechanical Engineering Design Optimization by Differential Evolution. Volume 1. London: McGraw-Hill, 1999.20 p, ISBN 007-709506-5Google Scholar
  2. [2]
    Siddal James N, Optimal engineering design: principles and application. Mechanical engineering series / 14. Marcel Dekker Inc. ISBN 0-8247-1633-7Google Scholar
  3. [3]
    Zelinka Ivan, Vladimir Vasek, Jouni Lampinen, New Algorithms of Global Optimization, Journal of Automation, Czech Ed., 10/01, 628-634, ISSN 0005-125XGoogle Scholar
  4. [4]
    Zelinka Ivan, Lampinen Jouni, SOMA - Self-Organizing Migrating Algorithm, Nostradamus 2000, 3rd International Conference on Prediction and Nonlinear Dynamic, Zlin, Czech RepublicGoogle Scholar
  5. [5]
    R. Storn and K. Price, „Differential Evolution - a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, 11(4):341-359, December 1997, Kulwer Academic PublisherszbMATHCrossRefMathSciNetGoogle Scholar
  6. [6]
    A. Neumaier, Complete Search in Continuous Global Optimization and Constraint Satisfaction, pp. 271-369 in: Acta Numerica 2004 (A. Iserles, ed.), Cambridge University Press 2004.Google Scholar
  7. [7]
    K. Hamacher. Adaptation in Stochastic Tunneling Global Optimization of Complex Potential Energy Landscapes, Europhys. Lett. 74(6):944, 2006.Google Scholar
  8. [8]
    Bernard ETKIN, Lloyd Duff Reid. Dynamics of flight – Stability and Control. John Wiley & Sons, Inc, 1996, ISBN: 978-0-471-03418-6Google Scholar
  9. [9]
    Tempo Roberto. Randomized Algorithms for Systems and Control: Theory and Applications, IEIIT-CNR, Politecnico di Torino, presentation: CTU Prague 2007Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

Personalised recommendations