Predictive control with mean derivative based neural euler integrator dynamic model
Autor(a) principal: | |
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Data de Publicação: | 2007 |
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=S0103-17592007000100007 |
Resumo: | Neural networks can be trained to get internal working models in dynamic systems control schemes. This has usually been done designing the neural network in the form of a discrete model with delayed inputs of the NARMA type (Non-linear Auto Regressive Moving Average). In recent works the use of the neural network inside the structure of ordinary differential equations (ODE) numerical integrators has also been considered to get dynamic systems discrete models. In this paper, an extension of this latter approach, where a feed forward neural network modeling mean derivatives is used in the structure of an Euler integrator, is presented and applied in a Nonlinear Predictive Control (NPC) scheme. The use of the neural network to approximate the mean derivative function, instead of the dynamic system ODE instantaneous derivative function, allows any specified accuracy to be attained in the modeling of dynamic systems with the use of a simple Euler integrator. This makes the predictive control implementation a simpler task, since it is only necessary to deal with the linear structure of a first order integrator in the calculations of control actions. To illustrate the effectiveness of the proposed approach, results of tests in a problem of orbit transfer between Earth and Mars and in a problem of three-axis attitude control of a rigid body satellite are presented. |
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Predictive control with mean derivative based neural euler integrator dynamic modelNeural ControlNonlinear Predictive ControlFeed Forward Neural NetsDynamic Systems Neural ModelingOrdinary Differential Equations Numerical IntegratorsNeural networks can be trained to get internal working models in dynamic systems control schemes. This has usually been done designing the neural network in the form of a discrete model with delayed inputs of the NARMA type (Non-linear Auto Regressive Moving Average). In recent works the use of the neural network inside the structure of ordinary differential equations (ODE) numerical integrators has also been considered to get dynamic systems discrete models. In this paper, an extension of this latter approach, where a feed forward neural network modeling mean derivatives is used in the structure of an Euler integrator, is presented and applied in a Nonlinear Predictive Control (NPC) scheme. The use of the neural network to approximate the mean derivative function, instead of the dynamic system ODE instantaneous derivative function, allows any specified accuracy to be attained in the modeling of dynamic systems with the use of a simple Euler integrator. This makes the predictive control implementation a simpler task, since it is only necessary to deal with the linear structure of a first order integrator in the calculations of control actions. To illustrate the effectiveness of the proposed approach, results of tests in a problem of orbit transfer between Earth and Mars and in a problem of three-axis attitude control of a rigid body satellite are presented.Sociedade Brasileira de Automática2007-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592007000100007Sba: Controle & Automação Sociedade Brasileira de Automatica v.18 n.1 2007reponame:Sba: Controle & Automação Sociedade Brasileira de Automaticainstname:Sociedade Brasileira de Automática (SBA)instacron:SBA10.1590/S0103-17592007000100007info:eu-repo/semantics/openAccessTasinaffo,Paulo M.Rios Neto,Ataireng2007-07-25T00:00:00Zoai:scielo:S0103-17592007000100007Revistahttps://www.sba.org.br/revista/PUBhttps://old.scielo.br/oai/scielo-oai.php||revista_sba@fee.unicamp.br1807-03450103-1759opendoar:2007-07-25T00:00Sba: Controle & Automação Sociedade Brasileira de Automatica - Sociedade Brasileira de Automática (SBA)false |
dc.title.none.fl_str_mv |
Predictive control with mean derivative based neural euler integrator dynamic model |
title |
Predictive control with mean derivative based neural euler integrator dynamic model |
spellingShingle |
Predictive control with mean derivative based neural euler integrator dynamic model Tasinaffo,Paulo M. Neural Control Nonlinear Predictive Control Feed Forward Neural Nets Dynamic Systems Neural Modeling Ordinary Differential Equations Numerical Integrators |
title_short |
Predictive control with mean derivative based neural euler integrator dynamic model |
title_full |
Predictive control with mean derivative based neural euler integrator dynamic model |
title_fullStr |
Predictive control with mean derivative based neural euler integrator dynamic model |
title_full_unstemmed |
Predictive control with mean derivative based neural euler integrator dynamic model |
title_sort |
Predictive control with mean derivative based neural euler integrator dynamic model |
author |
Tasinaffo,Paulo M. |
author_facet |
Tasinaffo,Paulo M. Rios Neto,Atair |
author_role |
author |
author2 |
Rios Neto,Atair |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Tasinaffo,Paulo M. Rios Neto,Atair |
dc.subject.por.fl_str_mv |
Neural Control Nonlinear Predictive Control Feed Forward Neural Nets Dynamic Systems Neural Modeling Ordinary Differential Equations Numerical Integrators |
topic |
Neural Control Nonlinear Predictive Control Feed Forward Neural Nets Dynamic Systems Neural Modeling Ordinary Differential Equations Numerical Integrators |
description |
Neural networks can be trained to get internal working models in dynamic systems control schemes. This has usually been done designing the neural network in the form of a discrete model with delayed inputs of the NARMA type (Non-linear Auto Regressive Moving Average). In recent works the use of the neural network inside the structure of ordinary differential equations (ODE) numerical integrators has also been considered to get dynamic systems discrete models. In this paper, an extension of this latter approach, where a feed forward neural network modeling mean derivatives is used in the structure of an Euler integrator, is presented and applied in a Nonlinear Predictive Control (NPC) scheme. The use of the neural network to approximate the mean derivative function, instead of the dynamic system ODE instantaneous derivative function, allows any specified accuracy to be attained in the modeling of dynamic systems with the use of a simple Euler integrator. This makes the predictive control implementation a simpler task, since it is only necessary to deal with the linear structure of a first order integrator in the calculations of control actions. To illustrate the effectiveness of the proposed approach, results of tests in a problem of orbit transfer between Earth and Mars and in a problem of three-axis attitude control of a rigid body satellite are presented. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007-03-01 |
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=S0103-17592007000100007 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-17592007000100007 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0103-17592007000100007 |
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.18 n.1 2007 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_ |
1754824564731281408 |