Combining a recurrent neural network and a PID controller for prognostic purpose: a way to improve the accuracy of predictions

Authors:
Ryad Zemouri;Rafael Gouriveau;Paul Ciprian Patic
Affiliations:
Laboratoire d'Automatique, Conservatoire National des Arts et Métaiers, Paris, France and Institut FEMTO-ST, UMR, CNRS, UFC, ENSMM, UTBM, Département Automatique et Systèmes Micro-M ...;Institut FEMTO-ST, UMR, CNRS, UFC, ENSMM, UTBM, Département Automatique et Systèmes Micro-Mécatronique, Besançon, France;Electrical Engineering Faculty, Valahia University Targoviste, Targoviste, Romania
Venue:
WSEAS Transactions on Systems and Control
Year:
2010

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Abstract

In maintenance field, prognostic is recognized as a key feature as the prediction of the remaining useful life of a system which allows avoiding inopportune maintenance spending. Assuming that it can be difficult to provide models for that purpose, artificial neural networks appear to be well suited. In this paper, an approach combining a Recurrent Radial Basis Function network (RRBF) and a proportional integral derivative controller (PID) is proposed in order to improve the accuracy of predictions. The PID controller attempts to correct the error between the real process variable and the neural network predictions.