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 language | English |
|---|---|
| Pages (from-to) | 1913-1922 |
| Number of pages | 10 |
| Journal | Automatica |
| Volume | 40 |
| Issue number | 11 |
| Early online date | 11 Aug 2004 |
| DOIs | |
| Publication status | Published - Nov 2004 |
| Externally published | Yes |
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