Constrained minimum variance control using hybrid genetic algorithm – An industrial experience
This paper develops a method for minimum variance control of proportional–integral (PI) controllers in the presence of input stochastic noise, the abatement of which is an important issue in many control systems. The underlying objective is to mitigate the effect of input noise in the process output...
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| Published in | Journal of process control Vol. 18; no. 1; pp. 36 - 44 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
2008
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0959-1524 1873-2771 |
| DOI | 10.1016/j.jprocont.2007.05.006 |
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| Abstract | This paper develops a method for minimum variance control of proportional–integral (PI) controllers in the presence of input stochastic noise, the abatement of which is an important issue in many control systems. The underlying objective is to mitigate the effect of input noise in the process output, subject to process inequality constraints. For this purpose, a hybrid genetic algorithm is used. It combines the genetic operations of selection, crossover, and mutation with Newton search. The developed method is applied in an industrial setting to find the optimal controller parameters of different control loops at Falconbridge Smelter in Sudbury, Canada. The optimal parameters significantly improve the performance of the PI controllers. |
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| AbstractList | This paper develops a method for minimum variance control of proportional–integral (PI) controllers in the presence of input stochastic noise, the abatement of which is an important issue in many control systems. The underlying objective is to mitigate the effect of input noise in the process output, subject to process inequality constraints. For this purpose, a hybrid genetic algorithm is used. It combines the genetic operations of selection, crossover, and mutation with Newton search. The developed method is applied in an industrial setting to find the optimal controller parameters of different control loops at Falconbridge Smelter in Sudbury, Canada. The optimal parameters significantly improve the performance of the PI controllers. |
| Author | Lohi, A. Upreti, S.R. Ein-Mozaffari, F. Hanna, J. |
| Author_xml | – sequence: 1 givenname: J. surname: Hanna fullname: Hanna, J. – sequence: 2 givenname: S.R. surname: Upreti fullname: Upreti, S.R. email: supreti@ryerson.ca – sequence: 3 givenname: A. surname: Lohi fullname: Lohi, A. – sequence: 4 givenname: F. surname: Ein-Mozaffari fullname: Ein-Mozaffari, F. |
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| CitedBy_id | crossref_primary_10_1016_j_isatra_2014_02_004 crossref_primary_10_32964_TJ7_5_3 crossref_primary_10_1016_j_jprocont_2012_07_015 crossref_primary_10_1016_j_isatra_2018_10_047 crossref_primary_10_1049_iet_cta_2017_0760 crossref_primary_10_3182_20110828_6_IT_1002_00295 crossref_primary_10_1016_j_isatra_2022_04_001 crossref_primary_10_1002_cjce_20653 crossref_primary_10_1016_j_cherd_2010_01_015 crossref_primary_10_1007_s11071_014_1617_5 crossref_primary_10_1016_j_jprocont_2008_12_005 |
| Cites_doi | 10.1002/aic.10104 10.1205/cherd05048 10.1016/S0005-1098(97)00017-4 10.1002/cjce.5450710413 10.1016/j.compchemeng.2003.09.003 10.1016/S0005-1098(96)00108-2 10.1016/S0959-1524(99)00031-1 10.1021/ie00014a009 10.1002/cjce.5450670519 |
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| References | Holland (bib10) 1975 Knuth (bib14) 1973; vol. 2 Huang, Shah, Kwok (bib6) 1997; 33 Stanfelj, Marlin, MacGregor (bib4) 1993; 32 Harris, Bourdeau, MacGregor (bib5) 1996; 32 Shinskey (bib15) 1996 Upreti (bib11) 2004; 28 Astrom (bib1) 1970 Goldberg (bib9) 1989 Harris (bib2) 1989; 67 Upreti, Ein-Mozaffari (bib12) 2006; 84 Desborough, Harris (bib3) 1993; 71 Horch, Alf (bib7) 1999; 9 Ko, Edgar (bib8) 2004; 50 Rao (bib13) 1996 Shinskey (10.1016/j.jprocont.2007.05.006_bib15) 1996 Harris (10.1016/j.jprocont.2007.05.006_bib2) 1989; 67 Ko (10.1016/j.jprocont.2007.05.006_bib8) 2004; 50 Astrom (10.1016/j.jprocont.2007.05.006_bib1) 1970 Huang (10.1016/j.jprocont.2007.05.006_bib6) 1997; 33 Horch (10.1016/j.jprocont.2007.05.006_bib7) 1999; 9 Knuth (10.1016/j.jprocont.2007.05.006_bib14) 1973; vol. 2 Holland (10.1016/j.jprocont.2007.05.006_bib10) 1975 Desborough (10.1016/j.jprocont.2007.05.006_bib3) 1993; 71 Goldberg (10.1016/j.jprocont.2007.05.006_bib9) 1989 Rao (10.1016/j.jprocont.2007.05.006_bib13) 1996 Upreti (10.1016/j.jprocont.2007.05.006_bib12) 2006; 84 Stanfelj (10.1016/j.jprocont.2007.05.006_bib4) 1993; 32 Harris (10.1016/j.jprocont.2007.05.006_bib5) 1996; 32 Upreti (10.1016/j.jprocont.2007.05.006_bib11) 2004; 28 |
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| SubjectTerms | Hybrid genetic algorithm Minimum variance PI controllers |
| Title | Constrained minimum variance control using hybrid genetic algorithm – An industrial experience |
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