Estimation of the precursor power and internal reactivity in a nuclear reactor by a neural observer

J. Humberto Pérez-Cruz, Alexander Poznyak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

This paper presents the design of a nonlinear robust observer for the estimation of the neutron precursor power and internal reactivity in a nuclear research reactor when only the input and the neutron power are available for measurement. The observer is based on a differential neural network with internal and external layers. Besides, this observer has two correction terms: Luenberger one and sliding mode one. This last term is intended to reduce the output external noise effect. The neural network is initially trained off-line using a very simplified third order nonlinear model of the nuclear reactor. The off-line training process is robust with respect to the model employed. Thus, when this preliminary training has finished, the neural observer can work as a completely physical model-free system and can carry out the on-line state estimation within a small margin of error despite uncertainty and noise. The efficiency of this technique with a guaranteed bound for the averaged estimation error is illustrated by simulation.

Original languageEnglish
Title of host publication2007 4th International Conference on Electrical and Electronics Engineering, ICEEE 2007
Pages310-313
Number of pages4
DOIs
StatePublished - 1 Dec 2007
Event2007 4th International Conference on Electrical and Electronics Engineering, ICEEE 2007 - Mexico City, Mexico
Duration: 5 Sep 20077 Sep 2007

Publication series

Name2007 4th International Conference on Electrical and Electronics Engineering, ICEEE 2007

Conference

Conference2007 4th International Conference on Electrical and Electronics Engineering, ICEEE 2007
Country/TerritoryMexico
CityMexico City
Period5/09/077/09/07

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