Implementación de controlador de vuelo para vehículos aéreos no tripulados multi-rotor basado en técnicas de aprendizaje profundo

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dc.contributor.advisorCamacho Poveda, Edgar Camilo
dc.contributor.advisorCalderón Chávez, Juan Manuel
dc.contributor.authorCárdenas Bohórquez, Javier Alexis
dc.contributor.authorCarrero Cuadrado, Uriel Eduardo
dc.contributor.corporatenameUniversidad Santo Tomásspa
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001630084spa
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000380938spa
dc.contributor.orcidhttps://orcid.org/0000-0002-6084-2512spa
dc.contributor.orcidhttps://orcid.org/ 0000-0002-4471-3980spa
dc.coverage.campusCRAI-USTA Bogotáspa
dc.date.accessioned2022-07-18T20:01:43Z
dc.date.available2022-07-18T20:01:43Z
dc.date.issued2022-06-22
dc.descriptionEste proyecto de grado presenta el diseño e implementación de un controlador de posición para un UAV multi-rotor basado en redes neuronales profundas y entrenado mediante aprendizaje supervisado, tomando como referencia un controlador PID. Se detalla el proceso de selección del entorno de simulación, el controlador y el modelo seleccionado. Así mismo, se realizan evaluaciones de trayectorias de control para la construcción de un conjunto de datos que permita entrenar el modelo. Se entrenan distintas arquitecturas de redes neuronales, mediante el uso del algoritmo Hyperband para determinar los mejores hiperparámetros. Finalmente se evalúa el rendimiento del controlador entrenado con respecto al controlador base mediante la respuesta temporal con diferentes señales de control. Como producto final se presenta: el conjunto de datos del controlador de referencia, un repositorio con los programas realizados para el desarrollo y análisis, y el modelo de la red neuronal.spa
dc.description.abstractThis degree project presents the design and implementation of a position controller for a multi-rotor UAV based on deep neural networks and trained by for a multi-rotor UAV based on deep neural networks and trained by means of supervised learning supervised learning, taking as reference a PID controller. It details the process of selection of the simulation environment, the controller and the selected model is detailed. Likewise, evaluations of control trajectories control trajectories evaluations for the construction of a data set to train the model. to train the model. Different neural network architectures are trained, using the Hyperband algorithm to determine the best hyperparameters. Finally, the performance of the trained controller is evaluated with respect to the base controller by means of the temporal response with different signals. the base controller by means of the time response with different control signals. As a final product, the following is presented the dataset of the reference controller, a repository with the programs developed for the development and analysis, and development and analysis programs, and the neural network model. Translated with www.DeepL.com/Translator (free version)spa
dc.description.degreelevelPregradospa
dc.description.degreenameIngeniero Electronicospa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationCárdenas Bohórquez, J. A. & Carrero Cuadrado, U. E. (2022).Implementación de controlador de vuelo para vehículos aéreos no tripulados multi-rotor basado en técnicas de aprendizaje profundo [Tesis de Pregrado en Ingeniería Electrónica, Universidad Santo Tomás] Repositorio Institucionalspa
dc.identifier.instnameinstname:Universidad Santo Tomásspa
dc.identifier.reponamereponame:Repositorio Institucional Universidad Santo Tomásspa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa
dc.identifier.urihttp://hdl.handle.net/11634/45916
dc.language.isospaspa
dc.publisherUniversidad Santo Tomásspa
dc.publisher.facultyFacultad de Ingeniería Electrónicaspa
dc.publisher.programPregrado Ingeniería Electrónicaspa
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dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.subject.keywordDronespa
dc.subject.keywordNeural Networkspa
dc.subject.keywordSupervised Trainingspa
dc.subject.keywordDeep Learningspa
dc.subject.keywordFlight Controllerspa
dc.subject.lembAviones no tripuladosspa
dc.subject.lembVehículos no tripuladosspa
dc.subject.lembAeronáuticaspa
dc.subject.lembSimuladores de vuelospa
dc.subject.lembDronesspa
dc.subject.proposalDronspa
dc.subject.proposalRed Neuronalspa
dc.subject.proposalAprendizaje Supervisadospa
dc.subject.proposalAprendizaje Profundospa
dc.subject.proposalControlador de Vuelospa
dc.titleImplementación de controlador de vuelo para vehículos aéreos no tripulados multi-rotor basado en técnicas de aprendizaje profundospa
dc.typebachelor thesis
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driveinfo:eu-repo/semantics/bachelorThesis
dc.type.localTesis de pregradospa
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

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