基于神经网络的多面体LPV系统鲁棒故障识别

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI:10.1109/ISIC.2014.6967628

M. Luzar, M. Witczak, M. Mrugalski, Zbigniew Kanski

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引用次数: 2

摘要

本文描述了一种基于人工神经网络的离散线性变参系统辨识方法。特别地，对神经网络进行变换，得到非线性系统的LPV模型。此外，针对一类非线性系统，提出了一种基于H∞框架观测器的鲁棒故障诊断方案。通过对多油箱系统的故障估计，验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Robust fault identification of a polytopic LPV system with neural network

In this paper, a discrete-time Linear Parameter-Varying (LPV) system identification method using artificial neural network is described. In particular, neural network is transformed to obtain LPV model of the non-linear system. Moreover, a novel robust fault diagnosis scheme is developed, which is based on an observer within H∞ framework for a class of non-linear systems. The effectiveness of the proposed approach is illustrated by the faults estimation in the multi-tank system.

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2014 IEEE International Symposium on Intelligent Control (ISIC)

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