Abstract
This article investigates a Cautious Minimum Variance (CMV) control approach for controlling industrial process variability when the model parameters are estimated from data and subject to uncertainty. CMV control has a number of advantages over traditional robust control methods. It incorporates probabilistic, as opposed to deterministic, measures of parameter uncertainty, which are more consistent with the statistical methods typically used to estimate industrial process models. CMV control is also more consistent with the objective of minimizing process variability, since parameter uncertainty is treated simply as an additional source of variation. CMV results have previously been derived for the case where the process disturbance follows a first-order integrated moving average model. This work extends the results to autoregressive moving average and autoregressive integrated moving average disturbances.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 417-432 |
| Number of pages | 16 |
| Journal | IIE Transactions (Institute of Industrial Engineers) |
| Volume | 36 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2004 |
Funding
This work was supported by the State of Texas Advanced Technology Program under grant 000512-0289-1999 and the National Science Foundation under grant DMI-0093580. The authors would also like to thank three anonymous referees for many helpful comments that have improved this article. Daniel W. Apley received B.S. and M.S. degrees in Mechanical Engineering, a M.S. degree in Electrical Engineering, and a Ph.D. degree in Mechanical Engineering in 1990, 1992, 1995 and 1997, respectively, all from the University of Michigan. From 1997 to 1998 he was a post-doctoral fellow with the Department of Industrial and Operations Engineering at the University of Michigan. Between 1998 and 2003 he was with Texas A&M University, where he was an Assistant Professor of Industrial Engineering. In 2003 he became an Associate Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. His research area is manufacturing variation reduction via statistical process monitoring, diagnosis and automatic control and the utilization of large sets of in-process measurement data. His current work is sponsored by Ford, Solectron, Applied Materials, the National Science Foundation and the State of Texas Advanced Technology Program. He was an AT&T Bell Laboratories Ph.D. Fellow from 1993 to 1997 and received the NSF CAREER award in 2001. He is a member of IIE, IEEE, ASME, INFORMS and SME.
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering