Indirect model reference adaptive control of quadrotor UAVs using neural networks

In this paper we propose an Artificial Neural Network (ANN) based controller for a quadrotor UAV. We use an Indirect Model Reference Adaptive Control scheme to show trajectory tracking in the presence of dynamically modeled thrust and drag coefficients. A plant emulator ANN continuously and accurate...

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Bibliographic Details
Published in2016 10th International Conference on Intelligent Systems and Control (ISCO) pp. 1 - 6
Main Authors Bakshi, Nikhil Angad, Ramachandran, Raghu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2016
Subjects
Adaptation models
Adaptive control
Aerodynamics
Cost function
MRAC
Neural networks
Online Learning
Propellers
Quadrotor
Training
UAV
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Summary:In this paper we propose an Artificial Neural Network (ANN) based controller for a quadrotor UAV. We use an Indirect Model Reference Adaptive Control scheme to show trajectory tracking in the presence of dynamically modeled thrust and drag coefficients. A plant emulator ANN continuously and accurately predicts the next plant output in addition to back propagating to compute the errors in the control inputs. The control inputs are generated based on the state error and its derivative by the controller ANN. Unknown or changing plant parameters are handled effectively in this manner.
DOI:10.1109/ISCO.2016.7727123