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

J. Humberto Pérez-Cruz, Alexander Poznyak

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

11 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2007 4th International Conference on Electrical and Electronics Engineering, ICEEE 2007
Páginas310-313
Número de páginas4
DOI
EstadoPublicada - 1 dic 2007
Evento2007 4th International Conference on Electrical and Electronics Engineering, ICEEE 2007 - Mexico City, México
Duración: 5 sep 20077 sep 2007

Serie de la publicación

Nombre2007 4th International Conference on Electrical and Electronics Engineering, ICEEE 2007

Conferencia

Conferencia2007 4th International Conference on Electrical and Electronics Engineering, ICEEE 2007
País/TerritorioMéxico
CiudadMexico City
Período5/09/077/09/07

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