A two-stage iterative learning control technique combined with real-time feedback for independent disturbance rejection

Insik CHIN, S. Joe QIN*, Kwang S. LEE, Moonki CHO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

121 Citations (Scopus)

Abstract

A novel control framework for batch and repetitive processes is proposed. The currently practiced methods to combine real-time feedback control (RFC) with iterative learning control (ILC) share a problem that RFC causes ILC to digress from its convergence track along the run index when there occur real-time disturbances. The proposed framework provides a pertinent means to incorporate RFC into ILC so that the performance of ILC is virtually separated from the effects of real-time disturbances. As a prototypical algorithm, a two-stage algorithm has been devised by modifying and combining the existing QILC and BMPC techniques. © 2004 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)1913-1922
Number of pages10
JournalAutomatica
Volume40
Issue number11
Early online date11 Aug 2004
DOIs
Publication statusPublished - Nov 2004
Externally publishedYes

Bibliographical note

The first author would like to acknowledge the financial support from Korea Science and Engineering Foundation (KOSEF) through the Post-doctoral Fellowship Program. SJQ acknowledges the financial support of the National Science Foundation CAREER Grant (CTS-9985074). KSL is grateful to the KOSEF and the Korea Research Foundation Grant (KRF-2002-042-D00028) for the financial support. The authors are grateful to the members of Texas–Wisconsin Modeling and Control Consortium (TWMCC) for their financial support.

Keywords

  • Batch process control
  • Iterative learning control
  • Model predictive control
  • Run-to-run control
  • Stochastic control

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