Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/10041,
  title    = {Adaptation Learning Speed Control for a High- Performance Induction Motor using Neural Networks},
  author    = {M. Zerikat and  S. Chekroun},
  country   = {},
  institution={},
  abstract  = {This paper proposes an effective adaptation learning
algorithm based on artificial neural networks for speed control of an
induction motor assumed to operate in a high-performance drives
environment. The structure scheme consists of a neural network
controller and an algorithm for changing the NN weights in order that
the motor speed can accurately track of the reference command. This
paper also makes uses a very realistic and practical scheme to
estimate and adaptively learn the noise content in the speed load
torque characteristic of the motor. The availability of the proposed
controller is verified by through a laboratory implementation and
under computation simulations with Matlab-software. The process is
also tested for the tracking property using different types of reference
signals. The performance and robustness of the proposed control
scheme have evaluated under a variety of operating conditions of the
induction motor drives. The obtained results demonstrate the
effectiveness of the proposed control scheme system performances,
both in steady state error in speed and dynamic conditions, was found
to be excellent and those is not overshoot.},
    journal   = {International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering},  volume    = {2},
  number    = {9},
  year      = {2008},
  pages     = {1791 - 1796},
  ee        = {http://waset.org/publications/10041},
  url       = {http://waset.org/Publications?p=21},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 21, 2008},
}