• DocumentCode
    1338960
  • Title

    ANNNAC-extension of adaptive backstepping algorithm with artificial neural networks

  • Author

    Knohl, T. ; Unbehauen, H.

  • Author_Institution
    Control Eng. Lab., Ruhr-Univ., Bochum, Germany
  • Volume
    147
  • Issue
    2
  • fYear
    2000
  • fDate
    3/1/2000 12:00:00 AM
  • Firstpage
    177
  • Lastpage
    183
  • Abstract
    The adaptive backstepping algorithm is a well-known scheme for the design of nonlinear adaptive controllers. The two main drawbacks associated with this algorithm are that the nonlinear system must be linearly parameterised in the unknown or uncertain parameters and that the nonlinear functions must be exactly known. To avoid these problems, an extension of the backstepping algorithm with a specific type of artificial neural networks (ANN) called radial basis function networks (RBF), is proposed. This extension leads to a new control scheme: namely artificial neural network nonlinear adaptive control (ANNNAC). To further clarify the approach, a simple example is studied and the simulation results demonstrate clearly the power of this extension
  • Keywords
    Lyapunov methods; adaptive control; control system synthesis; feedback; matrix algebra; neurocontrollers; nonlinear control systems; parameter estimation; radial basis function networks; ANNNAC; adaptive backstepping algorithm; artificial neural network nonlinear adaptive control;
  • fLanguage
    English
  • Journal_Title
    Control Theory and Applications, IEE Proceedings -
  • Publisher
    iet
  • ISSN
    1350-2379
  • Type

    jour

  • DOI
    10.1049/ip-cta:20000193
  • Filename
    843255