Neural identification and control for linear induction motors

  • Authors:

  • Victor H. Benitez;Edgar N. Sanchez;Alexander G. Loukianov

  • Affiliations:

  • CINVESTAV, Unidad Guadalajara, Apartado Postal 31-438, Plaza la Luna, Guadalajara, Jalisco C.P. 45091, México;CINVESTAV, Unidad Guadalajara, Apartado Postal 31-438, Plaza la Luna, Guadalajara, Jalisco C.P. 45091, México;CINVESTAV, Unidad Guadalajara, Apartado Postal 31-438, Plaza la Luna, Guadalajara, Jalisco C.P. 45091, México

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
  • Year:
  • 2005

  • Adaptive control based on neural network system identification

    EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology

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Abstract

We propose a new adaptive control scheme, composed of a neural identifier and a nonlinear controller and applied it to a linear induction motor (LIM). In order to compare the performance of LIM, we use α - β and d - q models. A neural identifier of triangular form is proposed for both models as a nonlinear block controllable form (NBC). Then, a reduced order observer is designed in order to estimate no measured variables. Learning law for neural network weights ensure that the identification error converges to zero exponentially. Sliding mode control is developed to track velocity and flux magnitude. Simulations are presented to compare the behaviour of both models of LIM and the applicability of the proposed identification and control scheme.