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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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