• 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