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Discrete-Time Recurrent High Order Neural Observer for Induction Motors

  • Edgar N. Sanchez
  • Alma Y. Alanis
  • Alexander G. Loukianov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4529)

Abstract

A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability simulation results are included.

Keywords

Induction Motor Load Torque Rotor Resistance Nonlinear Observer MIMO Nonlinear System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Edgar N. Sanchez
    • 1
  • Alma Y. Alanis
    • 2
  • Alexander G. Loukianov
    • 2
  1. 1.CINVESTAV, Unidad Guadalajara, on sabbatical leave at CUCEI, Universidad de GuadalajaraMexico
  2. 2.CINVESTAV, Unidad Guadalajara, Apartado Postal 31-438, Plaza La Luna, Guadalajara, Jalisco, C.P. 45091Mexico

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