• DocumentCode
    2581178
  • Title

    Control of an airship using particle swarm optimization and neural network

  • Author

    Jia, Ruting ; Frye, Michael T. ; Qian, Chunjiang

  • Author_Institution
    Electr. & Comput. Eng. Dept., Univ. of Texas at San Antonio, San Antonio, TX, USA
  • fYear
    2009
  • fDate
    11-14 Oct. 2009
  • Firstpage
    1809
  • Lastpage
    1814
  • Abstract
    The objective of this paper is to design an optimized controller for the tri-turbofan airship model. In lieu of using the traditional controller analysis method, the particle swarm optimization algorithm for controller optimization has been implemented. For more accurate results, this research used an updated neural network model to approximate the actual tri-turbofan airship dynamics. The effectiveness of the PSO algorithm will be shown by the simulation in an updated neural network model, compared to a linear model of the tri-turbofan model.
  • Keywords
    aerospace computing; aircraft control; control system analysis; control system synthesis; neural nets; optimal control; particle swarm optimisation; PSO algorithm; controller optimization; neural network; particle swarm optimization; real time optimal control; tri-turbofan airship dynamics; tri-turbofan airship model; Cellular phones; Design engineering; Design optimization; Evolutionary computation; Neural networks; Optimal control; Particle swarm optimization; Surveillance; USA Councils; Vehicle dynamics; Particle swarm optimization; dynamic neural network model; real time optimal control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
  • Conference_Location
    San Antonio, TX
  • ISSN
    1062-922X
  • Print_ISBN
    978-1-4244-2793-2
  • Electronic_ISBN
    1062-922X
  • Type

    conf

  • DOI
    10.1109/ICSMC.2009.5346862
  • Filename
    5346862