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Neuronal Implementation of Predictive Controllers

  • José Manuel López-Guede
  • Ekaitz Zulueta
  • Borja Fernández-Gauna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

In spite of the multiple advantages that Model Predictive Control offers (for example, they can control systems that classical control schemes can’t), it has a main drawback: it is computationally expensive in its working phase. In this paper we deal with the problem of getting an implementation of predictive controllers that implements its operations in an efficient way, so we use a neuronal implementation. We show how we have trained these neural networks, and how we exploit their generalization property and their robustness when there are control and measurement disturbances.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • José Manuel López-Guede
    • 1
  • Ekaitz Zulueta
    • 1
  • Borja Fernández-Gauna
    • 1
  1. 1.Computational Intelligence Group UPV/EHU 

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