Controlling Aluminum Strip Thickness by Clustered Reinforcement Learning With Real-World Dataset-Reference-Cited by-同舟云学术

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Controlling Aluminum Strip Thickness by Clustered Reinforcement Learning With Real-World Dataset

Published:2024-08 Issue:8 Volume:20 Page:9928-9938
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Xiao Ziqi¹^ORCID,He Zhili¹^ORCID,Liang Huanghuang¹^ORCID,Hu Chuang¹^ORCID,Cheng Dazhao¹^ORCID

Affiliation:

1. School of Computer Science, Wuhan University, Wuhan, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Science and Technology Development Fund, Macao S.A.R

General Program of Hubei Provincial Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10623532/10510190.pdf?arnumber=10510190

Reference35 articles.

1. Characteristic analysis and optimal control of the thickness and tension system on tandem cold rolling

2. Understanding automatic generation control

3. Research progress and intelligent trend of accurate modeling of rolling force in metal sheet

4. Design of a fault-tolerant output-feedback controller for thickness control in cold rolling mills

5. Influence mechanism of rolling force on strip shape during tandem hot rolling using a novel 3D multi-stand coupled thermo-mechanical FE model

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