DocumentCode
1752802
Title
Diagonal Recurrent Neural Network Decoupling Control on the Loopers´ Height and Tension System
Author
Li, Bo-qun ; Zhang, Ke-jun ; Fu, Jian ; Sun, Yi-kang
Author_Institution
Inf. Eng. Coll., Univ. of Sci. & Technol. Beijing
Volume
1
fYear
0
fDate
0-0 0
Firstpage
2787
Lastpage
2791
Abstract
For improving further automatic gauge control accuracy and quality in hot strip mills, this paper gives appropriate mathematics model and whole transferring functions based on coupling process analysis and practical date in order to complete decoupling control of the loopers´ height and tension system. The control strategy based on DRNN is introduced to decouple the looper system. It can cope with changes in rolling conditions by controlling the rotational speed of the main motor in addition to the existing looper motor current. The simulation results have shown the effectiveness of this algorithm and after decoupled, the loopers´ control performance gets much better
Keywords
gauges; hot rolling; mechanical variables control; neurocontrollers; recurrent neural nets; rolling mills; transfer functions; automatic gauge control; building model; coupling process analysis; diagonal recurrent neural network decoupling control; hot strip mills; looper height; looper motor current; looper tension system; mathematical model; motor rotational speed; transfer function; Automatic control; Control systems; Couplings; Educational institutions; Milling machines; Recurrent neural networks; Signal analysis; Stress; Strips; Sun; Building model; DRNN; Decoupling; Looper system;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location
Dalian
Print_ISBN
1-4244-0332-4
Type
conf
DOI
10.1109/WCICA.2006.1712872
Filename
1712872
Link To Document