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Neural Evolutionary Predictive Control for Linear Induction Motors with Experimental Data

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

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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.

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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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Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Blvd. Marcelino Garcia Barragan 1500, Col. Olimpica, Guadalajara, Jalisco, C.P, 44430, Mexico

    Alma Y. Alanis, Nancy Arana-Daniel, Carlos Lopez-Franco & Jorge D. Rios

Authors
  1. Alma Y. Alanis
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  2. Nancy Arana-Daniel
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  3. Carlos Lopez-Franco
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  4. Jorge D. Rios
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Corresponding author

Correspondence to Alma Y. Alanis .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Alanis, A.Y., Arana-Daniel, N., Lopez-Franco, C., Rios, J.D. (2020). Neural Evolutionary Predictive Control for Linear Induction Motors with Experimental Data. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_28

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