Nonlinear reconfigurable control based on RBF neural networks

被引：0

作者：

Zhou, C ^{[1
]}

Hu, WL ^{[1
]}

Chen, QW ^{[1
]}

Wang, Y ^{[1
]}

Hu, SS ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Dept Automat, Nanjing 210094, Peoples R China

来源：

PROCEEDINGS OF THE 3RD WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-5 | 2000年

关键词：

reconfigurable control; neural networks; model-following;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new type of non-linear reconfigurable control strategy based on model-following method using Radial basis function (RBF) neural networks is presented in this paper. This method can make the outputs of impaired system tracking those of reference model accurately without knowing the location and damage degree of failure, and a RBF neural network controller is used to compensate non-linear dynamics caused by failure. Simulation results reveal that this method has good reconfigurable performance and robustness.

引用

页码：1002 / 1005

页数：4

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