Premium
Integrating statistical process monitoring with feedforward control
Author(s) -
Montgomery Douglas C.,
Keats J. B.,
Yatskievitch Mark,
Messina William S.
Publication year - 2000
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/1099-1638(200011/12)16:6<515::aid-qre359>3.0.co;2-i
Subject(s) - feed forward , control theory (sociology) , cusum , control variable , variable (mathematics) , statistical process control , controller (irrigation) , process (computing) , control (management) , feedforward neural network , computer science , mathematics , statistics , control engineering , engineering , artificial intelligence , artificial neural network , mathematical analysis , agronomy , biology , operating system
Feedforward control is a particular form of engineering process control. If a known input variable z t can be measured and appropriate relationships built between the input variable, a compensatory variable x t , and the desired output y t , then a feedforward control system can be developed. Using feedforward control, we show under both sudden jump shifts and trends in the mean of the input and compensatory variables that the use of statistical process monitoring tools, such as the exponentially‐weighted moving average and the cumulative sum (CUSUM), significantly reduces variability in both the output variable and the controller relative to the use of feedforward control alone. Copyright © 2000 John Wiley & Sons, Ltd.