Synthesis of model predictive control based on data-driven learning

被引：0

作者：

Yuanqiang ZHOU ^{[1
,2
,3
]}

Dewei LI ^{[2
,3
]}

Yugeng XI ^{[2
,3
]}

Zhongxue GAN ^{[1
]}

机构：

[1] ENN Science & Technology Development Co.,Ltd

[2] Department of Automation,Shanghai Jiao Tong University

[3] Key Laboratory of System Control and Information Processing,Ministry of Education of China

来源：

Science China(Information Sciences) | 2020年 / 63卷 / 08期

基金：

上海市自然科学基金; 中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP13 [自动控制理论];

学科分类号：

0711 ; 071102 ; 0811 ; 081101 ; 081103 ;

摘要：

Dear editor,Model predictive control (MPC) is a practically effective and attractive approach in the field of industrial processes [1] owing to its excellent ability to handle constraints, nonlinearity, and performance/cost trade-offs. The core of all model-based predictive algorithms is to use "open-loop optimal control" instead of "closed-loop optimal control"within a moving horizon [2]. It is assumed in this

引用

页码：251 / 253

页数：3

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