• DocumentCode
    358940
  • Title

    Dynamically structured radial basis function neural networks for robust aircraft flight control

  • Author

    Yan, Li ; Sundarajan, N. ; Saratchandran, P.

  • Author_Institution
    Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore
  • Volume
    5
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    3501
  • Abstract
    An online control scheme that utilizes a dynamically structured radial basis function network (RBFN) is developed for aircraft control. By using Lyapunov synthesis approach, the tuning rule for updating all the parameters of the dynamic RBFN which guarantees the stability of the overall system is derived. The robustness of the proposed tuning rule is also analyzed. Simulation studies using the F8 aircraft longitudinal model demonstrates the efficiency of the method and also show that with a dynamically structured RBFN, a more compact network structure can be implemented
  • Keywords
    Lyapunov methods; aircraft control; control system synthesis; military aircraft; neurocontrollers; nonlinear control systems; online operation; radial basis function networks; robust control; F8 aircraft longitudinal model; Lyapunov synthesis; RBFN; dynamically structured radial basis function neural networks; online control scheme; robust aircraft flight control; simulation studies; Aerospace control; Aerospace electronics; Aircraft propulsion; Control systems; Military aircraft; Neural networks; Nonlinear systems; Radial basis function networks; Robust control; Robust stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 2000. Proceedings of the 2000
  • Conference_Location
    Chicago, IL
  • ISSN
    0743-1619
  • Print_ISBN
    0-7803-5519-9
  • Type

    conf

  • DOI
    10.1109/ACC.2000.879220
  • Filename
    879220