Neuronal implementation of predictive controllers

Authors:
José Manuel López-Guede;Ekaitz Zulueta;Borja Fernández-Gauna
Affiliations:
Computational Intelligence Group UPV/EHU;Computational Intelligence Group UPV/EHU;Computational Intelligence Group UPV/EHU
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Year:
2010

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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.