• DocumentCode
    2599281
  • Title

    An ANN optimal preview controller technique for induction motor

  • Author

    Negm, M.M.M. ; Mantawy, A.H.

  • Author_Institution
    Dept. of Electr. Eng., Ain-Shams Univ., Cairo, Egypt
  • Volume
    2
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    966
  • Abstract
    An artificial neural networks (ANN) technique for on-line speed control of a three-phase induction motor (IM) is presented in this paper. This novel technique is based on the optimal preview controller. The proposed technique comprises a new error system and vector control of the IM. Preview feedforward steps are introduced into the control law to enhance the transient response and to improve the robustness of the controlled system. A feedforward neural network trained with the backpropagation algorithm has been developed to embody the characteristic of the above optimal preview controller within a small, accurate and global system. The training was successful over a large range of training data. Test results conducted over several data ranges have shown accurate and fast performance in predicting the controller output variables
  • Keywords
    angular velocity control; backpropagation; feedforward neural nets; induction motors; machine vector control; neurocontrollers; optimal control; predictive control; transient response; ANN optimal preview controller; artificial neural networks; backpropagation algorithm; error system; feedforward; feedforward neural network; induction motor; on-line speed control; robustness improvement; three-phase induction motor; transient response enhancement; vector control; Artificial neural networks; Control systems; Error correction; Induction motors; Machine vector control; Neural networks; Optimal control; Robust control; Transient response; Velocity control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industry Applications Conference, 2000. Conference Record of the 2000 IEEE
  • Conference_Location
    Rome
  • ISSN
    0197-2618
  • Print_ISBN
    0-7803-6401-5
  • Type

    conf

  • DOI
    10.1109/IAS.2000.881949
  • Filename
    881949