A multi-core software design of a model predictive control for a tilt-rotor UAV

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dc.creatorGabriela M. T. Miranda
dc.creatorLeandro Miranda Fahur Machado
dc.creatorJanier Arias-Garcia
dc.creatorGuilherme Vianna Raffo
dc.date.accessioned2025-03-26T15:31:23Z
dc.date.accessioned2025-09-09T00:46:37Z
dc.date.available2025-03-26T15:31:23Z
dc.date.issued2017
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.urihttps://hdl.handle.net/1843/80950
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofXIII Simpósio Brasileiro de Automação Inteligente
dc.rightsAcesso Aberto
dc.subjectEngenharia elétrica
dc.subjectControle preditivo
dc.subjectControle robusto
dc.subjectTransferência de funções
dc.subjectIncerteza
dc.subject.otherSoftware Architecture, Multi-core, MPC, Embedded Systems
dc.titleA multi-core software design of a model predictive control for a tilt-rotor UAV
dc.typeArtigo de evento
local.citation.epage7
local.citation.spage1
local.description.resumoThis paper addresses the implementation of an embedded model predictive controller (MPC) designed to solve the path tracking problem of a tilt-rotor unmanned aerial vehicle (UAV). The architecture of the system is presented, describing its functional and non-functional features. The development of a dual-core software architecture for implementing an MPC is proposed, making use of the Robot Operating System (ROS) framework and the Gazebo simulator to obtain the numerical results. The software architecture approach is designed in order to improve the time MPC performance using the multiple processors simultaneously.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sba.org.br/Proceedings/SBAI/SBAI2017/SBAI17/papers/paper_706.pdf

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