Direct adaptive control using feedforward neural networks
Autor(a) principal: | |
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Data de Publicação: | 2003 |
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=S0103-17592003000400002 |
Resumo: | This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application. |
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Sba: Controle & Automação Sociedade Brasileira de Automatica |
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|
spelling |
Direct adaptive control using feedforward neural networksAdaptive controlbackpropagationconvergenceextended Kalman filterneural networksstabilityThis paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application.Sociedade Brasileira de Automática2003-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592003000400002Sba: Controle & Automação Sociedade Brasileira de Automatica v.14 n.4 2003reponame:Sba: Controle & Automação Sociedade Brasileira de Automaticainstname:Sociedade Brasileira de Automática (SBA)instacron:SBA10.1590/S0103-17592003000400002info:eu-repo/semantics/openAccessCajueiro,Daniel OliveiraHemerly,Elder Moreiraeng2004-04-15T00:00:00Zoai:scielo:S0103-17592003000400002Revistahttps://www.sba.org.br/revista/PUBhttps://old.scielo.br/oai/scielo-oai.php||revista_sba@fee.unicamp.br1807-03450103-1759opendoar:2004-04-15T00:00Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA)false |
dc.title.none.fl_str_mv |
Direct adaptive control using feedforward neural networks |
title |
Direct adaptive control using feedforward neural networks |
spellingShingle |
Direct adaptive control using feedforward neural networks Cajueiro,Daniel Oliveira Adaptive control backpropagation convergence extended Kalman filter neural networks stability |
title_short |
Direct adaptive control using feedforward neural networks |
title_full |
Direct adaptive control using feedforward neural networks |
title_fullStr |
Direct adaptive control using feedforward neural networks |
title_full_unstemmed |
Direct adaptive control using feedforward neural networks |
title_sort |
Direct adaptive control using feedforward neural networks |
author |
Cajueiro,Daniel Oliveira |
author_facet |
Cajueiro,Daniel Oliveira Hemerly,Elder Moreira |
author_role |
author |
author2 |
Hemerly,Elder Moreira |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Cajueiro,Daniel Oliveira Hemerly,Elder Moreira |
dc.subject.por.fl_str_mv |
Adaptive control backpropagation convergence extended Kalman filter neural networks stability |
topic |
Adaptive control backpropagation convergence extended Kalman filter neural networks stability |
description |
This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application. |
publishDate |
2003 |
dc.date.none.fl_str_mv |
2003-12-01 |
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=S0103-17592003000400002 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592003000400002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-17592003000400002 |
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.14 n.4 2003 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_ |
1754824563977355264 |