Receding-horizon control method with recurrent neural networks - 文章详情页

作者：

Li, Ming-Ai (Li, Ming-Ai.) (学者：李明爱) | Qiao, Jun-Fei (Qiao, Jun-Fei.) (学者：乔俊飞) | Ruan, Xiao-Gang (Ruan, Xiao-Gang.)

收录：

EI Scopus PKU CSCD

摘要：

A　linear　difference　Hopfield　neural　network　(LDHNN)　is　built,　and　its　energy　function　can　reach　the　only　minimum　while　LDHNN　is　stable.　With　the　use　of　the　relation　between　the　stability　and　energy　function　convergence　of　the　Hopfield　neural　network,　an　LDHNN-based　receding-horizon　(RH)　control　method　is　proposed.　The　theoretical　design　of　LDHNN　shows　that　the　stable　outputs　of　LDHNN　are　the　solution　of　the　RH　LQ　control　problem.　The　LDHNN-based　RH　control　can　also　guarantee　the　asymptotical　stability　of　closed-loop　optimal　control　systems　if　the　controlled　systems　satisfy　certain　conditions.　The　numerical　simulation　results　show　the　correction　of　theoretical　analysis.

关键词：

Asymptotic stability Closed loop control systems Computer simulation Linear control systems Optimal control systems Recurrent neural networks

作者机构：

[ 1 ] [Li, Ming-Ai]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 2 ] [Qiao, Jun-Fei]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China
[ 3 ] [Ruan, Xiao-Gang]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100022, China

通讯作者信息：

电子邮件地址：

adrxg@bjut.edu.cn

查看成果更多字段

成果类型
所属机构

所有年份指定年份从至