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Neural Evolutionary Predictive Control for Linear Induction Motors with Experimental Data

  • Alma Y. AlanisEmail author
  • Nancy Arana-Daniel
  • Carlos Lopez-Franco
  • Jorge D. Rios
Chapter
  • 43 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 862)

Abstract

Model Predictive Control is a well-suited control strategy; however, it needs to solve two main problems in order to be applied, first, it is necessary to have a suitable model of the plant to be controlled, in order to allow an adequate prediction, second, it is necessary to solve an optimization problem. These two problems not always can be solved particularly for nonlinear complex systems. Therefore, in this chapter we propose two variations for Model Predictive Control, in a first stage a recurrent high order neural network is proposed to obtain a fitting model for the plant to be controlled, and, at the same time this neural model identifies the system on-line through available measurements. Then, in a second stage, the optimization problem is solved using a particle swarm optimization algorithm. Using these two modifications, it is proposed a Neural Evolutionary Predictive Control for discrete-time nonlinear systems under disturbances under disturbances, and its effectiveness is shown in the experimental results by using data obtained from a linear induction motor prototype.

Keywords

Model predictive control Neural evolutionary predictive control Linear induction motors Experimental data Kalman filter learning 

Notes

Acknowledgements

Authors thank the support of CONACYT Mexico, through Projects CB256769 and CB258068 (Project supported by Fondo Sectorial de Investigacion para la Educacion).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alma Y. Alanis
    • 1
    Email author
  • Nancy Arana-Daniel
    • 1
  • Carlos Lopez-Franco
    • 1
  • Jorge D. Rios
    • 1
  1. 1.CUCEI, Universidad de GuadalajaraGuadalajaraMexico

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