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Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm

Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight control...

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Main Author: Gómez López, Víctor Adolfo (author)
Other Authors: Gomez, Nicolas (author), Rodas Benítez, Jorge Esteban (author), Paiva, Enrique (author), Saad, Maarouf (author), Gregor Recalde, Raúl Igmar (author)
Format: article
Language:English
Published: 2020
Subjects:
4. Transporte, telecomunicaciones y otras infraestructuras
UNMANNED AERIAL VEHICLE (UAV)
MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
PARETO FRONT
PROPORTIONAL-INTEGRAL-DERIVATIVE (PID)
INGENIERIA
Online Access:http://hdl.handle.net/20.500.14066/3647
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author Gómez López, Víctor Adolfo
author2 Gomez, Nicolas
Rodas Benítez, Jorge Esteban
Paiva, Enrique
Saad, Maarouf
Gregor Recalde, Raúl Igmar
author2_role author
author
author
author
author
author_browse Gomez, Nicolas
Gregor Recalde, Raúl Igmar
Gómez López, Víctor Adolfo
Paiva, Enrique
Rodas Benítez, Jorge Esteban
Saad, Maarouf
author_facet Gómez López, Víctor Adolfo
Gomez, Nicolas
Rodas Benítez, Jorge Esteban
Paiva, Enrique
Saad, Maarouf
Gregor Recalde, Raúl Igmar
author_role author
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bitstream.url.fl_str_mv http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3647/1/PINV15-136art9.pdf
http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3647/2/license.txt
http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3647/3/PINV15-136art9.pdf.txt
dc.contributor.other.es.fl_str_mv Universidad Nacional de Asunción - Facultad de Ingeniería
dc.creator.none.fl_str_mv Gómez López, Víctor Adolfo
Gomez, Nicolas
Rodas Benítez, Jorge Esteban
Paiva, Enrique
Saad, Maarouf
Gregor Recalde, Raúl Igmar
dc.date.accessioned.none.fl_str_mv 2022-04-26T01:08:34Z
dc.date.available.none.fl_str_mv 2022-04-26T01:08:34Z
dc.date.issued.none.fl_str_mv 2020
dc.identifier.doi.es.fl_str_mv 10.3390/aerospace7060071
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.14066/3647
dc.language.iso.es.fl_str_mv eng
dc.relation.projectCONACYT.es.fl_str_mv PINV15-136
dc.rights.accessRights.es.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.classification.es.fl_str_mv 4. Transporte, telecomunicaciones y otras infraestructuras
dc.subject.ocde.es.fl_str_mv INGENIERIA
dc.subject.other.es.fl_str_mv UNMANNED AERIAL VEHICLE (UAV)
MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
PARETO FRONT
PROPORTIONAL-INTEGRAL-DERIVATIVE (PID)
dc.title.es.fl_str_mv Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm
dc.type.es.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
description Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice.
eu_rights_str_mv openAccess
format article
id CONACYT_3415d8125a8bbf18af6e24bdc19ae382
identifier_str_mv 10.3390/aerospace7060071
language eng
network_acronym_str CONACYT
network_name_str Repositorio Institucional CONACYT
oai_identifier_str oai:repositorio.conacyt.gov.py:20.500.14066/3647
publishDate 2020
publishDateSort 2020
repository.mail.fl_str_mv repositorio.institucional@conacyt.gov.py
repository.name.fl_str_mv Repositorio Institucional CONACYT
repository_id_str
spelling 525600fa052415-15ad-4219-af4a-d6ed1e1b3a5560097560074ebcfee-142c-4f2b-9b20-48364ad54cf0600325a404e-1c07-4e00-b114-382fc08a8ac76005696000000-0002-7717-4100Universidad Nacional de Asunción - Facultad de Ingeniería2022-04-26T01:08:34Z2022-04-26T01:08:34Z2020http://hdl.handle.net/20.500.14066/364710.3390/aerospace7060071Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice.Consejo Nacional de Ciencia y TecnologíaPROCIENCIAeng4. Transporte, telecomunicaciones y otras infraestructurasUNMANNED AERIAL VEHICLE (UAV)MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATIONPARETO FRONTPROPORTIONAL-INTEGRAL-DERIVATIVE (PID)INGENIERIAPareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion71Aerospace120PINV15-136info:eu-repo/semantics/openAccess7Gómez López, Víctor AdolfoGomez, NicolasRodas Benítez, Jorge EstebanPaiva, EnriqueSaad, MaaroufGregor Recalde, Raúl IgmarORIGINALPINV15-136art9.pdfPINV15-136art9.pdfapplication/pdf27426880http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3647/1/PINV15-136art9.pdf01e6f4cadf0b1f65d4af6e8be8f0b8f4MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81698http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3647/2/license.txt858b22fda432bd774e469302988c1974MD52TEXTPINV15-136art9.pdf.txtPINV15-136art9.pdf.txtExtracted texttext/plain55933http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3647/3/PINV15-136art9.pdf.txtb2af87a59c806d500f0d2809fa434855MD5320.500.14066/3647oai:repositorio.conacyt.gov.py:20.500.14066/36472026-02-12 19:30:29.594Repositorio Institucional CONACYTrepositorio.institucional@conacyt.gov.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
spellingShingle Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm
Gómez López, Víctor Adolfo
4. Transporte, telecomunicaciones y otras infraestructuras
UNMANNED AERIAL VEHICLE (UAV)
MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
PARETO FRONT
PROPORTIONAL-INTEGRAL-DERIVATIVE (PID)
INGENIERIA
status_str publishedVersion
title Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm
title_full Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm
title_fullStr Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm
title_full_unstemmed Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm
title_short Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm
title_sort Pareto optimal PID tuning for Px4-Based unmanned aerial vehicles by using a multi-objective particle swarm optimization algorithm
topic 4. Transporte, telecomunicaciones y otras infraestructuras
UNMANNED AERIAL VEHICLE (UAV)
MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION
PARETO FRONT
PROPORTIONAL-INTEGRAL-DERIVATIVE (PID)
INGENIERIA
url http://hdl.handle.net/20.500.14066/3647

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