• DocumentCode
    495078
  • Title

    Solving the Steady Flight State of Aircraft Based on Hybrid Genetic Algorithm

  • Author

    Zhibo, Luan ; Qitao, Huang ; Hongzhou, Jiang ; Hongren, Li

  • Author_Institution
    Sch. of Mechatron. Eng., Harbin Inst. of Technol., Harbin, China
  • Volume
    2
  • fYear
    2009
  • fDate
    21-22 May 2009
  • Firstpage
    200
  • Lastpage
    203
  • Abstract
    Steady flat flight is widely used in the flight simulator training as an ideal initial state. To ensure the accurate solving of the steady flat flight state a hybrid genetic algorithm is put forward. The algorithm based on the new concept of ldquoindividual learning potentialityrdquo make the Lamarckian learning and Baldwinina learning genetic algorithm combination together organically according to the particularity of the solving in the steady flat flight state. The algorithm could make the advantage of the learning into full play and make the disadvantage into inhibitory. The algorithm has generality which just use the state variable to calculate and can be independent of the airplane dynamic. Simulation result shows that the new algorithm combined the tow learning mechanism has made a good effect.
  • Keywords
    aerospace simulation; aircraft; genetic algorithms; learning (artificial intelligence); Baldwinina learning; Lamarckian learning; aircraft; flight simulator training; hybrid genetic algorithm; steady flight state; Aerodynamics; Aerospace engineering; Aerospace simulation; Aircraft propulsion; Genetic algorithms; Genetic engineering; Mathematical model; Mechatronics; Nonlinear equations; Steady-state; Baldwinina learning; Genetic algorithm; Individual learning potentiality; Lamarckian learning; Steady flight state;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Computing Science, 2009. ICIC '09. Second International Conference on
  • Conference_Location
    Manchester
  • Print_ISBN
    978-0-7695-3634-7
  • Type

    conf

  • DOI
    10.1109/ICIC.2009.159
  • Filename
    5169043