Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs

Autores
Murillo, Marina Hebe; Limache, Alejandro Cesar; Rojas Fredini, Pablo Sebastián; Giovanini, Leonardo Luis
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Model Predictive Control (MPC) is a modern technique that, nowadays, encapsulates different optimal control techniques. For the case of non-linear dynamics, many possible variants can be developed which can lead to new control algorithms. In this manuscript a novel generic control system method is presented. This method can be applied to control, in an optimal way, different systems having non-linear dynamics. Particularly, in this paper, the proposed technique is presented in the context of developing a control system for autonomous flight of UAVs. This technique can be used for different types of aerial vehicles having any type of generic non-linear dynamics. The presented method is based on the use of iteratively defined optimal candidate state-space trajectories in global state-space. The method uses a generalized linearization process which, opposite to standard methods, does not need to be predefined in a certain equilibrium state but instead it is performed along any arbitrary state. The technique allows the inclusion of constraints with ease. The presented technique is used as a centralized control system unit that is able to control the full aircraft dynamics without the need of decoupling the system in different reduced modes. The technique is tested by making a Cessna 172 airplane model to perform the following autonomous unmanned maneuvers: climbing at constant speed to a desired altitude, heading change to a desired flight direction, and, coordinate turn.
Fil: Murillo, Marina Hebe. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Limache, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Rojas Fredini, Pablo Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Materia
Uav
Non-Linear Predictive Control
Navigation And Control
Model Predictive Control
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/44508
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVsMurillo, Marina HebeLimache, Alejandro CesarRojas Fredini, Pablo SebastiánGiovanini, Leonardo LuisUavNon-Linear Predictive ControlNavigation And ControlModel Predictive Controlhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Model Predictive Control (MPC) is a modern technique that, nowadays, encapsulates different optimal control techniques. For the case of non-linear dynamics, many possible variants can be developed which can lead to new control algorithms. In this manuscript a novel generic control system method is presented. This method can be applied to control, in an optimal way, different systems having non-linear dynamics. Particularly, in this paper, the proposed technique is presented in the context of developing a control system for autonomous flight of UAVs. This technique can be used for different types of aerial vehicles having any type of generic non-linear dynamics. The presented method is based on the use of iteratively defined optimal candidate state-space trajectories in global state-space. The method uses a generalized linearization process which, opposite to standard methods, does not need to be predefined in a certain equilibrium state but instead it is performed along any arbitrary state. The technique allows the inclusion of constraints with ease. The presented technique is used as a centralized control system unit that is able to control the full aircraft dynamics without the need of decoupling the system in different reduced modes. The technique is tested by making a Cessna 172 airplane model to perform the following autonomous unmanned maneuvers: climbing at constant speed to a desired altitude, heading change to a desired flight direction, and, coordinate turn.Fil: Murillo, Marina Hebe. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Limache, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Rojas Fredini, Pablo Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaInst Control Robotics & Systems2015-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/44508Murillo, Marina Hebe; Limache, Alejandro Cesar; Rojas Fredini, Pablo Sebastián; Giovanini, Leonardo Luis; Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs; Inst Control Robotics & Systems; International Journal Of Control Automation And Systems; 13; 2; 4-2015; 361-3701598-6446CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s12555-013-0416-yinfo:eu-repo/semantics/altIdentifier/doi/10.1007/s12555-013-0416-yinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:37:51Zoai:ri.conicet.gov.ar:11336/44508instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:37:51.344CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs
title Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs
spellingShingle Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs
Murillo, Marina Hebe
Uav
Non-Linear Predictive Control
Navigation And Control
Model Predictive Control
title_short Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs
title_full Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs
title_fullStr Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs
title_full_unstemmed Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs
title_sort Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs
dc.creator.none.fl_str_mv Murillo, Marina Hebe
Limache, Alejandro Cesar
Rojas Fredini, Pablo Sebastián
Giovanini, Leonardo Luis
author Murillo, Marina Hebe
author_facet Murillo, Marina Hebe
Limache, Alejandro Cesar
Rojas Fredini, Pablo Sebastián
Giovanini, Leonardo Luis
author_role author
author2 Limache, Alejandro Cesar
Rojas Fredini, Pablo Sebastián
Giovanini, Leonardo Luis
author2_role author
author
author
dc.subject.none.fl_str_mv Uav
Non-Linear Predictive Control
Navigation And Control
Model Predictive Control
topic Uav
Non-Linear Predictive Control
Navigation And Control
Model Predictive Control
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Model Predictive Control (MPC) is a modern technique that, nowadays, encapsulates different optimal control techniques. For the case of non-linear dynamics, many possible variants can be developed which can lead to new control algorithms. In this manuscript a novel generic control system method is presented. This method can be applied to control, in an optimal way, different systems having non-linear dynamics. Particularly, in this paper, the proposed technique is presented in the context of developing a control system for autonomous flight of UAVs. This technique can be used for different types of aerial vehicles having any type of generic non-linear dynamics. The presented method is based on the use of iteratively defined optimal candidate state-space trajectories in global state-space. The method uses a generalized linearization process which, opposite to standard methods, does not need to be predefined in a certain equilibrium state but instead it is performed along any arbitrary state. The technique allows the inclusion of constraints with ease. The presented technique is used as a centralized control system unit that is able to control the full aircraft dynamics without the need of decoupling the system in different reduced modes. The technique is tested by making a Cessna 172 airplane model to perform the following autonomous unmanned maneuvers: climbing at constant speed to a desired altitude, heading change to a desired flight direction, and, coordinate turn.
Fil: Murillo, Marina Hebe. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Limache, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Rojas Fredini, Pablo Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
description Model Predictive Control (MPC) is a modern technique that, nowadays, encapsulates different optimal control techniques. For the case of non-linear dynamics, many possible variants can be developed which can lead to new control algorithms. In this manuscript a novel generic control system method is presented. This method can be applied to control, in an optimal way, different systems having non-linear dynamics. Particularly, in this paper, the proposed technique is presented in the context of developing a control system for autonomous flight of UAVs. This technique can be used for different types of aerial vehicles having any type of generic non-linear dynamics. The presented method is based on the use of iteratively defined optimal candidate state-space trajectories in global state-space. The method uses a generalized linearization process which, opposite to standard methods, does not need to be predefined in a certain equilibrium state but instead it is performed along any arbitrary state. The technique allows the inclusion of constraints with ease. The presented technique is used as a centralized control system unit that is able to control the full aircraft dynamics without the need of decoupling the system in different reduced modes. The technique is tested by making a Cessna 172 airplane model to perform the following autonomous unmanned maneuvers: climbing at constant speed to a desired altitude, heading change to a desired flight direction, and, coordinate turn.
publishDate 2015
dc.date.none.fl_str_mv 2015-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/44508
Murillo, Marina Hebe; Limache, Alejandro Cesar; Rojas Fredini, Pablo Sebastián; Giovanini, Leonardo Luis; Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs; Inst Control Robotics & Systems; International Journal Of Control Automation And Systems; 13; 2; 4-2015; 361-370
1598-6446
CONICET Digital
CONICET
url http://hdl.handle.net/11336/44508
identifier_str_mv Murillo, Marina Hebe; Limache, Alejandro Cesar; Rojas Fredini, Pablo Sebastián; Giovanini, Leonardo Luis; Generalized nonlinear optimal predictive control using iterative state-space trajectories: Applications to autonomous flight of UAVs; Inst Control Robotics & Systems; International Journal Of Control Automation And Systems; 13; 2; 4-2015; 361-370
1598-6446
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s12555-013-0416-y
info:eu-repo/semantics/altIdentifier/doi/10.1007/s12555-013-0416-y
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Inst Control Robotics & Systems
publisher.none.fl_str_mv Inst Control Robotics & Systems
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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