Implementación de controlador de vuelo para vehículos aéreos no tripulados multi-rotor basado en técnicas de aprendizaje profundo
Fecha
2022-06-22Registro en:
Cárdenas 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 Institucional
reponame:Repositorio Institucional Universidad Santo Tomás
instname:Universidad Santo Tomás
Autor
Cárdenas Bohórquez, Javier Alexis
Carrero Cuadrado, Uriel Eduardo
Institución
Resumen
This 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.
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