A Model-following Adaptive Controller for Linear/Nonlinear Plants using Radial Basis Function Neural Networks

被引：0

作者：

Masukake, Yuichi ^{[1
]}

Ishida, Yoshihisa ^{[1
]}

机构：

[1] Meiji Univ, Grad Sch Elect Engn, Tokyo 101, Japan

来源：

PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 26, PARTS 1 AND 2, DECEMBER 2007 | 2007年 / 26卷

关键词：

Linear/nonlinear plants; neural networks; radial basis function networks;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, we proposed a method to design a model-following adaptive controller for linear/nonlinear plants. Radial basis function neural networks (RBF-NNs), which are known for their stable learning capability and fast training, are used to identify linear/nonlinear plants. Simulation results show that the proposed method is effective in controlling both linear and nonlinear plants with disturbance in the plant input.

引用

页码：407 / +

页数：2

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