Attitude and Altitude Control of Quadcopter Maneuvers using Neural Network–Based Direct Inverse Control
In this study, a neural network–based direct inverse control (DIC) approach was developed and simulated to control various maneuvers of an unmanned aerial vehicle (UAV) quadcopter. The aim was to propose an inner loop control algorithm for UAV quadcopter maneuvers using a neural network–based DI...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Universitas Indonesia
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/74f997706ef04550809c6239e26b223c |
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| Sumario: | In
this study, a neural network–based direct inverse control (DIC) approach was
developed and simulated to control various maneuvers of an unmanned aerial
vehicle (UAV) quadcopter. The aim was to propose an inner loop control
algorithm for UAV quadcopter maneuvers using a neural network–based DIC system.
The appropriate connection weights of neurons in the controller were determined
through a backpropagation learning algorithm using real quadcopter maneuver
flight data. The neural network–based DIC was trained and then tested using a
trajectory dataset different from the training dataset. The experimental
results showed that the neural network–based DIC could follow the maneuvers of
the testing trajectory dataset with excellent performance, as indicated by an
overall mean squared error (MSE) of 1.461 and attitude MSEs of 3.104 for roll,
0.889 for pitch, 1.834 for yaw and 0.018 for altitude. These results indicate
that the proposed artificial neural network–based DIC can be used to control
the attitude and altitude of the quadcopter during maneuvers. |
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