System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs

Autores
Recalde Simancas, Luis Fernando; Guevara Bermeo, Bryan Stefano; Carvajal Cabrera, Christian Patricio; Andaluz Ortiz, Victor Hugo; Varela Aldás, José; Gandolfo, Daniel
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller.
Fil: Recalde Simancas, Luis Fernando. Universidad Tecnologica Indoamerica.; Ecuador
Fil: Guevara Bermeo, Bryan Stefano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carvajal Cabrera, Christian Patricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Andaluz Ortiz, Victor Hugo. Universidad de Las Fuerzas Armadas; Ecuador
Fil: Varela Aldás, José. Universidad Tecnologica Indoamerica.; Ecuador. Universidad de Zaragoza. Facultad de Ciencias; España
Fil: Gandolfo, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Materia
SYSTEM IDENTIFICATION
MODEL PREDICTIVE CONTROL
OBSTACLE AVOIDANCE
HEXACOPTER UAV
SYSTEM CONSTRAINTS
OPTIMIZATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/210833
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/210833
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVsRecalde Simancas, Luis FernandoGuevara Bermeo, Bryan StefanoCarvajal Cabrera, Christian PatricioAndaluz Ortiz, Victor HugoVarela Aldás, JoséGandolfo, DanielSYSTEM IDENTIFICATIONMODEL PREDICTIVE CONTROLOBSTACLE AVOIDANCEHEXACOPTER UAVSYSTEM CONSTRAINTSOPTIMIZATIONhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller.Fil: Recalde Simancas, Luis Fernando. Universidad Tecnologica Indoamerica.; EcuadorFil: Guevara Bermeo, Bryan Stefano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carvajal Cabrera, Christian Patricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Andaluz Ortiz, Victor Hugo. Universidad de Las Fuerzas Armadas; EcuadorFil: Varela Aldás, José. Universidad Tecnologica Indoamerica.; Ecuador. Universidad de Zaragoza. Facultad de Ciencias; EspañaFil: Gandolfo, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaMolecular Diversity Preservation International2022-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/210833Recalde Simancas, Luis Fernando; Guevara Bermeo, Bryan Stefano; Carvajal Cabrera, Christian Patricio; Andaluz Ortiz, Victor Hugo; Varela Aldás, José; et al.; System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs; Molecular Diversity Preservation International; Sensors; 22; 4712; 7-2022; 1-291424-8220CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1424-8220/22/13/4712info:eu-repo/semantics/altIdentifier/doi/10.3390/s22134712info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:06:25Zoai:ri.conicet.gov.ar:11336/210833instacron: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 10:06:25.42CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
title System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
spellingShingle System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
Recalde Simancas, Luis Fernando
SYSTEM IDENTIFICATION
MODEL PREDICTIVE CONTROL
OBSTACLE AVOIDANCE
HEXACOPTER UAV
SYSTEM CONSTRAINTS
OPTIMIZATION
title_short System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
title_full System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
title_fullStr System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
title_full_unstemmed System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
title_sort System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs
dc.creator.none.fl_str_mv Recalde Simancas, Luis Fernando
Guevara Bermeo, Bryan Stefano
Carvajal Cabrera, Christian Patricio
Andaluz Ortiz, Victor Hugo
Varela Aldás, José
Gandolfo, Daniel
author Recalde Simancas, Luis Fernando
author_facet Recalde Simancas, Luis Fernando
Guevara Bermeo, Bryan Stefano
Carvajal Cabrera, Christian Patricio
Andaluz Ortiz, Victor Hugo
Varela Aldás, José
Gandolfo, Daniel
author_role author
author2 Guevara Bermeo, Bryan Stefano
Carvajal Cabrera, Christian Patricio
Andaluz Ortiz, Victor Hugo
Varela Aldás, José
Gandolfo, Daniel
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv SYSTEM IDENTIFICATION
MODEL PREDICTIVE CONTROL
OBSTACLE AVOIDANCE
HEXACOPTER UAV
SYSTEM CONSTRAINTS
OPTIMIZATION
topic SYSTEM IDENTIFICATION
MODEL PREDICTIVE CONTROL
OBSTACLE AVOIDANCE
HEXACOPTER UAV
SYSTEM CONSTRAINTS
OPTIMIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller.
Fil: Recalde Simancas, Luis Fernando. Universidad Tecnologica Indoamerica.; Ecuador
Fil: Guevara Bermeo, Bryan Stefano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Carvajal Cabrera, Christian Patricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Andaluz Ortiz, Victor Hugo. Universidad de Las Fuerzas Armadas; Ecuador
Fil: Varela Aldás, José. Universidad Tecnologica Indoamerica.; Ecuador. Universidad de Zaragoza. Facultad de Ciencias; España
Fil: Gandolfo, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
description Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller.
publishDate 2022
dc.date.none.fl_str_mv 2022-07
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/11336/210833
Recalde Simancas, Luis Fernando; Guevara Bermeo, Bryan Stefano; Carvajal Cabrera, Christian Patricio; Andaluz Ortiz, Victor Hugo; Varela Aldás, José; et al.; System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs; Molecular Diversity Preservation International; Sensors; 22; 4712; 7-2022; 1-29
1424-8220
CONICET Digital
CONICET
url http://hdl.handle.net/11336/210833
identifier_str_mv Recalde Simancas, Luis Fernando; Guevara Bermeo, Bryan Stefano; Carvajal Cabrera, Christian Patricio; Andaluz Ortiz, Victor Hugo; Varela Aldás, José; et al.; System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs; Molecular Diversity Preservation International; Sensors; 22; 4712; 7-2022; 1-29
1424-8220
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1424-8220/22/13/4712
info:eu-repo/semantics/altIdentifier/doi/10.3390/s22134712
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
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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