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
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
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页码:251 / 253
页数:3
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