Nonlinear control system design using variable complexity modelling and multiobjective optimization
Autor(a) principal: | |
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Data de Publicação: | 2006 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Sba: Controle & Automação Sociedade Brasileira de Automatica |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592006000100003 |
Resumo: | To design controllers for complex non-linear systems usually involves the use of expensive computational models. A non-linear thermodynamic model of a gas turbine engine is used to evaluate a selection of designs for a multivariable PI controller configuration. An approach using variable complexity modelling (VCM) is introduced to allow more designs to be evaluated and also to speed up the design process. Response surface methodology (RSM) is a statistical technique in which smooth functions are used to model an objective function. RSM employs statistical methods to create functions, typically polynomials, to model the response or outcome of a numerical experiment in terms of several independent variables. Regression analysis is applied to fit polynomial models to this data for various control responses. These control responses models are evaluated by a multiobjective genetic algorithm to design the controller parameters. The final designs are checked using the original non-linear model. |
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Nonlinear control system design using variable complexity modelling and multiobjective optimizationMultiobjective genetic algorithmsoptimizationnon-linear systemsPI controllervariable complexity modellingTo design controllers for complex non-linear systems usually involves the use of expensive computational models. A non-linear thermodynamic model of a gas turbine engine is used to evaluate a selection of designs for a multivariable PI controller configuration. An approach using variable complexity modelling (VCM) is introduced to allow more designs to be evaluated and also to speed up the design process. Response surface methodology (RSM) is a statistical technique in which smooth functions are used to model an objective function. RSM employs statistical methods to create functions, typically polynomials, to model the response or outcome of a numerical experiment in terms of several independent variables. Regression analysis is applied to fit polynomial models to this data for various control responses. These control responses models are evaluated by a multiobjective genetic algorithm to design the controller parameters. The final designs are checked using the original non-linear model.Sociedade Brasileira de Automática2006-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592006000100003Sba: Controle & Automação Sociedade Brasileira de Automatica v.17 n.1 2006reponame:Sba: Controle & Automação Sociedade Brasileira de Automaticainstname:Sociedade Brasileira de Automática (SBA)instacron:SBA10.1590/S0103-17592006000100003info:eu-repo/semantics/openAccessSilva,Valceres V. R. EKhatib,WaelFleming,Peter J.eng2006-09-15T00:00:00Zoai:scielo:S0103-17592006000100003Revistahttps://www.sba.org.br/revista/PUBhttps://old.scielo.br/oai/scielo-oai.php||revista_sba@fee.unicamp.br1807-03450103-1759opendoar:2006-09-15T00:00Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA)false |
dc.title.none.fl_str_mv |
Nonlinear control system design using variable complexity modelling and multiobjective optimization |
title |
Nonlinear control system design using variable complexity modelling and multiobjective optimization |
spellingShingle |
Nonlinear control system design using variable complexity modelling and multiobjective optimization Silva,Valceres V. R. E Multiobjective genetic algorithms optimization non-linear systems PI controller variable complexity modelling |
title_short |
Nonlinear control system design using variable complexity modelling and multiobjective optimization |
title_full |
Nonlinear control system design using variable complexity modelling and multiobjective optimization |
title_fullStr |
Nonlinear control system design using variable complexity modelling and multiobjective optimization |
title_full_unstemmed |
Nonlinear control system design using variable complexity modelling and multiobjective optimization |
title_sort |
Nonlinear control system design using variable complexity modelling and multiobjective optimization |
author |
Silva,Valceres V. R. E |
author_facet |
Silva,Valceres V. R. E Khatib,Wael Fleming,Peter J. |
author_role |
author |
author2 |
Khatib,Wael Fleming,Peter J. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Silva,Valceres V. R. E Khatib,Wael Fleming,Peter J. |
dc.subject.por.fl_str_mv |
Multiobjective genetic algorithms optimization non-linear systems PI controller variable complexity modelling |
topic |
Multiobjective genetic algorithms optimization non-linear systems PI controller variable complexity modelling |
description |
To design controllers for complex non-linear systems usually involves the use of expensive computational models. A non-linear thermodynamic model of a gas turbine engine is used to evaluate a selection of designs for a multivariable PI controller configuration. An approach using variable complexity modelling (VCM) is introduced to allow more designs to be evaluated and also to speed up the design process. Response surface methodology (RSM) is a statistical technique in which smooth functions are used to model an objective function. RSM employs statistical methods to create functions, typically polynomials, to model the response or outcome of a numerical experiment in terms of several independent variables. Regression analysis is applied to fit polynomial models to this data for various control responses. These control responses models are evaluated by a multiobjective genetic algorithm to design the controller parameters. The final designs are checked using the original non-linear model. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-03-01 |
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=S0103-17592006000100003 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592006000100003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-17592006000100003 |
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 |
Sociedade Brasileira de Automática |
publisher.none.fl_str_mv |
Sociedade Brasileira de Automática |
dc.source.none.fl_str_mv |
Sba: Controle & Automação Sociedade Brasileira de Automatica v.17 n.1 2006 reponame:Sba: Controle & Automação Sociedade Brasileira de Automatica instname:Sociedade Brasileira de Automática (SBA) instacron:SBA |
instname_str |
Sociedade Brasileira de Automática (SBA) |
instacron_str |
SBA |
institution |
SBA |
reponame_str |
Sba: Controle & Automação Sociedade Brasileira de Automatica |
collection |
Sba: Controle & Automação Sociedade Brasileira de Automatica |
repository.name.fl_str_mv |
Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA) |
repository.mail.fl_str_mv |
||revista_sba@fee.unicamp.br |
_version_ |
1754824564372668416 |