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dc.contributor.advisorGonzález Querubín, Edwinspa
dc.contributor.authorJojoa Gómez, Juan Fernandospa
dc.contributor.authorMoreno Cárdenas, Sergio Fabiánspa
dc.date.accessioned2017-06-06T14:03:47Zspa
dc.date.accessioned2017-06-24T16:35:16Zspa
dc.date.available2017-06-06T14:03:47Zspa
dc.date.available2017-06-24T16:35:16Zspa
dc.date.issued2016spa
dc.identifier.urihttps://hdl.handle.net/11634/1801spa
dc.descriptionEl presente trabajo consiste en la implementación de un método de control predictivo basado en modelo (MPC), para el control de posición de drone, en este proyecto se utiliza un parrot A.R Drone 2.0. El algoritmo de control predictivo utilizadp en este proyecto es el, MPC en espacio de estados, el cual depende del modelo en espacio de estados de cada una de las variables a controlar: altitud, roll, pitch y yaw, también se realizan las respectivas simulaciones por medio del software Matlab, para su posterior implementación en LAbview. El proyecto se divide es desarrollado en varias fases, las cuales consisten en obtener los modelos matemáticos de cada una de las variables a controlar, así como el control de cada una de estas por separado, y por último se desarrolla el algoitmo de control multivariable.spa
dc.description.abstractThis work involves the implementation of model predictive control (MPC) method to control a UAV position. In this project, a parrot A.R Drone 2.0 is used. The predictive control algorithm used in this project is the state space MPC, which depends on the state space model of each controlled variables. This algorithm was developed for different control variables: altitude, roll, pitch and yaw. The respective simulations were made by Matlab software for subsequent implementation in the Labview software. The primary goal of predictive control is to solve the problems of process control efficiently. These processes may exhibit complex dynamic behaviors, such as the coupled variables, instability and even restrictions on some variables. The strategy of this type of control is to use a mathematical model of the process and thus obtain a series of predictions of the future behavior of the process. Based on these predictions and a reference for the controlled variable, future control signals are calculated so that the variable converge in their respective reference values, and respecting different restrictions in the system variable. To meet the objective of controlling the position of the drone, the project is developed in several phases, which are: obtain mathematical models of each variable to monitor and control each of them separately. Initially develop and test predictive control algorithm for SISO state-space system, checking the operation, simulated and implemented in the drone. The second step is the implementation of the algorithm for a MIMO system, which will control the position of the UAV.spa
dc.format.mimetypeapplication/pdfspa
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleControl de posición de un UAV mediante una estrategia de control predictivo para labores de monitoreospa
dc.description.degreenameIngeniero Mecatrónicospa
dc.subject.lembInnovaciones tecnológicasspa
dc.subject.lembSistemas linealesspa
dc.subject.lembControles de dispositivospa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.coverage.campusCRAI-USTA Bucaramangaspa
dc.subject.proposalControl predictivospa
dc.subject.proposalEspacio de datosspa
dc.subject.proposalObservador de estadosspa
dc.description.degreelevelPregradospa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa


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Atribución-NoComercial-SinDerivadas 2.5 Colombia
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