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Aprendizaje de máquina aplicado al control

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dc.contributor.advisorObando Bravo, Germán Dario
dc.creatorRambaut Lemus, Daniel Felipe
dc.creator.degreeProfesional en Matemáticas Aplicadas y Ciencias de la Computación
dc.creator.degreeLevelPregrado
dc.creator.degreetypeFull time
dc.date.accessioned2023-03-24T21:09:59Z
dc.date.available2023-03-24T21:09:59Z
dc.date.created2023-02-17
dc.descriptionEl objetivo de este trabajo es emular la acción de un controlador utilizando modelos de inteligencia artificial (IA). Para ello, se empleó como planta un sistema de segundo orden que describe la temperatura en un cuarto. Sobre dicha planta, se diseña un controlador predictivo basado en modelo (MPC, por sus siglas en inglés) como referencia para entrenar los algoritmos de IA. MPC es un método que utiliza modelos matemáticos para predecir el comportamiento futuro del sistema y tomar acciones de control óptimas en función de ciertos objetivos preestablecidos. La emulación del controlador puede plantearse como un problema de regresión, por lo tanto se emplearon tres de los modelos más populares de IA para efectuar regresiones: regresión lineal, vectores de soporte y redes neuronales. Para el entrenamiento de los modelos de IA, se utilizó una base de datos generada al simular el comportamiento del controlador MPC sobre la planta de temperatura. Se realizaron diferentes pruebas para evaluar el desempeño de los modelos de IA comparándolos con el controlador MPC. Los resultados mostraron que los modelos de IA pueden ser utilizados con éxito para emular dicho controlador con la ventaja de tener un menor costo computacional. En este sentido, cabe resaltar que MPC necesita resolver iterativamente un problema de optimización, mientras que los algoritmos de IA usados sólo requieren evaluar cierta función (que se obtiene al entrenar los modelos) en cada iteración de control. En conclusión, esta investigación es un primer paso exitoso en un camino prometedor: el uso de IA para el control de procesos dinámicos.
dc.description.abstractThe objective of this work is to emulate the action of a controller using artificial intelligence (AI) models. For this purpose, a second-order system that describes the temperature in a room was employed as the plant. On this plant, a model-based predictive controller (MPC) was designed as a reference to train the AI algorithms. MPC is a method that uses mathematical models to predict the future behavior of the system and take optimal control actions based on certain pre-established objectives. The emulation of the controller can be formulated as a regression problem, therefore, three of the most popular AI models were used for regression: linear regression, support vectors, and neural networks. To train the AI models, a database generated by simulating the behavior of the MPC controller on the temperature plant was used. Different tests were carried out to evaluate the performance of the AI models, comparing them with the MPC controller. The results showed that AI models can be successfully used to emulate the controller with the advantage of having lower computational costs. In this sense, it is worth noting that MPC needs to iteratively solve an optimization problem, while the AI algorithms used only require evaluating a certain function (which is obtained by training the models) at each control iteration. In conclusion, this research is a successful first step in a promising path: the use of AI for the control of dynamic processes.
dc.format.extent39 pp
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_38270
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/38270
dc.language.isospa
dc.publisherUniversidad del Rosario
dc.publisher.departmentEscuela de Ingeniería, Ciencia y Tecnología
dc.publisher.programPrograma de Matemáticas Aplicadas y Ciencias de la Computación - MACC
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
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dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subjectMachine learning
dc.subjectTeoria de control
dc.subjectAprendizaje automático
dc.subjectControl
dc.subjectModelos de inteligencia artificial (IA)
dc.subjectControlador predictivo basado en modelo MPC
dc.subjectUso de IA para el control de procesos dinámicos
dc.subject.keywordMachine learning
dc.subject.keywordControl theory
dc.subject.keywordControl
dc.titleAprendizaje de máquina aplicado al control
dc.title.TranslatedTitleMachine Learning Applied to Control
dc.typebachelorThesis
dc.type.documentTrabajo de grado
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTrabajo de grado
local.department.reportEscuela de Ciencias e Ingeniería
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