• DocumentCode
    184124
  • Title

    On inferential Iterative Learning Control: With example to a printing system

  • Author

    Bolder, Joost ; Oomen, Tom ; Steinbuch, Maarten

  • Author_Institution
    Dept. of Mech. Eng., Eindhoven Univ. of Technol., Eindhoven, Netherlands
  • fYear
    2014
  • fDate
    4-6 June 2014
  • Firstpage
    1827
  • Lastpage
    1832
  • Abstract
    Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example.
  • Keywords
    adaptive control; iterative methods; learning systems; sensors; batch-wise sensors; inferential ILC; inferential iterative learning control; performance improvement; performance variable measurement; performance variables; printing system; Adaptive control; Feedback control; Feedforward neural networks; Observers; Performance analysis; Position measurement; Real-time systems; Iterative learning control; Mechatronics; Optimal control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2014
  • Conference_Location
    Portland, OR
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4799-3272-6
  • Type

    conf

  • DOI
    10.1109/ACC.2014.6858946
  • Filename
    6858946