Adaptive MNN control for a class of non-affine NARMAX systems with disturbances

被引：79

作者：

Ge, SS ^{[1
]}

Zhang, J ^{[1
]}

Lee, TH ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore

来源：

SYSTEMS & CONTROL LETTERS | 2004年 / 53卷 / 01期

关键词：

non-affine nonlinear system; implicit function theorem; multi-layer neural networks; projection algorithm;

D O I：

10.1016/j.sysconle.2004.02.016

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, adaptive multi-layer neural network (MNN) control is developed for a class of discrete-time non-affine nonlinear systems in nonlinear auto regressive moving average with eXogenous inputs (NARMAX) model. By using implicit function theorem, the existence of the implicit desired feedback control (IDFC) is proved. MNNs are used as the emulator of the desired feedback control. Projection algorithms are used to guarantee the boundedness of the neural network (NN) weights, which removes the need of persistent exciting (PE) condition for parameter convergence. Simulation results show the effectiveness of the proposed control scheme. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：1 / 12

页数：12

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