Active Adaptive Combustion Control Using Neural Networks
作者:
R. BLONBOU,
A. LAVERDANT,
S. ZALESKI,
P. KUENTZMANN,
期刊:
Combustion Science and Technology
(Taylor Available online 2000)
卷期:
Volume 156,
issue 1
页码: 25-47
ISSN:0010-2202
年代: 2000
DOI:10.1080/00102200008947295
出版商: Taylor & Francis Group
关键词: adaptive control;neural networks;combustion instabilities
数据来源: Taylor
摘要:
The suppression of pressure oscillations in combustion chambers through the use of active feedback control is a new technology with high potential. In this article, we present a feedback control strategy based on an Internal Model Control System for nonlinear plants that uses artificial neural networks. This control system uses two neural networks: The Internal Model which approximates the plant forward dynamic; and a controller which gives the appropriate control input. The controller's parameters are updated adaptively for that purpose. We demonstrate numerically the capabilities of the developped control system in a numerical simulation of control of combustion instabilities. Then, we demonstrate the ability of this neural networks based control system to actively damp instabilities in a Rijke-tube burner.
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