Nonlinear control system design using variable complexity modelling and multiobjective optimization

Detalhes bibliográficos
Autor(a) principal: Silva,Valceres V. R. E
Data de Publicação: 2006
Outros Autores: Khatib,Wael, Fleming,Peter J.
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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spelling 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
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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)
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