DocumentCode :
1356423
Title :
Algorithms for industrial model predictive control
Author :
Sandoz, David J. ; Desforges, Matthew J. ; Lennox, Barry ; Goulding, Peter R.
Author_Institution :
Sch. of Eng., Manchester Univ., UK
Volume :
11
Issue :
3
fYear :
2000
fDate :
6/1/2000 12:00:00 AM
Firstpage :
125
Lastpage :
134
Abstract :
The article is concerned with control methods that have been embedded in an industrial model predictive control software package and that have been applied to a wide variety of industrial processes. Three methods are described and the various features are evaluated by considering a constrained multivariable simulation. One method has been in use since 1988 and is widely exploited in industry. The latest methods employ quadratic programming, which has become realistic to employ because of the advances in computing. The relative attributes are contrasted by assessing the ability of the controllers to recover effectively from the impact of a large unmeasured disturbance.
Keywords :
process control; constrained multivariable simulation; control methods; industrial model predictive control algorithms; industrial process control; large unmeasured disturbance; quadratic programming; relative attributes; software package;
fLanguage :
English
Journal_Title :
Computing & Control Engineering Journal
Publisher :
iet
ISSN :
0956-3385
Type :
jour
DOI :
10.1049/cce:20000306
Filename :
850787
Link To Document :
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