Stable receding horizon control based on recurrent networks

被引：8

作者：

Kambhampati, C ^{[1
]}

Delgado, A ^{[1
]}

Mason, JD ^{[1
]}

Warwick, K ^{[1
]}

机构：

[1] Univ Reading, Dept Cybernet, Reading RG6 2AB, Berks, England

来源：

IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS | 1997年 / 144卷 / 03期

关键词：

neural networks; recurrent; receding horizon; feedback; control; stability; optimality;

D O I：

10.1049/ip-cta:19970950

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The last decade has seen the reemergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

引用

页码：249 / 254

页数：6

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