Optimal control of a CSTR process
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Chemical Engineering |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322008000400017 |
Resumo: | Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC). The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO). Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications. |
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Brazilian Journal of Chemical Engineering |
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Optimal control of a CSTR processIntelligent controlGenetic learningPPDCReduced rule baseDesigning an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC). The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO). Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.Brazilian Society of Chemical Engineering2008-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322008000400017Brazilian Journal of Chemical Engineering v.25 n.4 2008reponame:Brazilian Journal of Chemical Engineeringinstname:Associação Brasileira de Engenharia Química (ABEQ)instacron:ABEQ10.1590/S0104-66322008000400017info:eu-repo/semantics/openAccessSoukkou,A.Khellaf,A.Leulmi,S.Boudeghdegh,K.eng2008-11-24T00:00:00Zoai:scielo:S0104-66322008000400017Revistahttps://www.scielo.br/j/bjce/https://old.scielo.br/oai/scielo-oai.phprgiudici@usp.br||rgiudici@usp.br1678-43830104-6632opendoar:2008-11-24T00:00Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ)false |
dc.title.none.fl_str_mv |
Optimal control of a CSTR process |
title |
Optimal control of a CSTR process |
spellingShingle |
Optimal control of a CSTR process Soukkou,A. Intelligent control Genetic learning PPDC Reduced rule base |
title_short |
Optimal control of a CSTR process |
title_full |
Optimal control of a CSTR process |
title_fullStr |
Optimal control of a CSTR process |
title_full_unstemmed |
Optimal control of a CSTR process |
title_sort |
Optimal control of a CSTR process |
author |
Soukkou,A. |
author_facet |
Soukkou,A. Khellaf,A. Leulmi,S. Boudeghdegh,K. |
author_role |
author |
author2 |
Khellaf,A. Leulmi,S. Boudeghdegh,K. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Soukkou,A. Khellaf,A. Leulmi,S. Boudeghdegh,K. |
dc.subject.por.fl_str_mv |
Intelligent control Genetic learning PPDC Reduced rule base |
topic |
Intelligent control Genetic learning PPDC Reduced rule base |
description |
Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC). The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO). Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322008000400017 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322008000400017 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0104-66322008000400017 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
publisher.none.fl_str_mv |
Brazilian Society of Chemical Engineering |
dc.source.none.fl_str_mv |
Brazilian Journal of Chemical Engineering v.25 n.4 2008 reponame:Brazilian Journal of Chemical Engineering instname:Associação Brasileira de Engenharia Química (ABEQ) instacron:ABEQ |
instname_str |
Associação Brasileira de Engenharia Química (ABEQ) |
instacron_str |
ABEQ |
institution |
ABEQ |
reponame_str |
Brazilian Journal of Chemical Engineering |
collection |
Brazilian Journal of Chemical Engineering |
repository.name.fl_str_mv |
Brazilian Journal of Chemical Engineering - Associação Brasileira de Engenharia Química (ABEQ) |
repository.mail.fl_str_mv |
rgiudici@usp.br||rgiudici@usp.br |
_version_ |
1754213172713095168 |