Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

In this article, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. First, the dynamic model of the quadcopter is considered under external disturbances. Sliding mode controllers are then integrated with neural netw...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.40076-40085
Main Authors: Nguyen, Ngoc Phi, Mung, Nguyen Xuan, Thanh, Ha Le Nhu Ngoc, Huynh, Tuan Tu, Lam, Ngoc Tam, Hong, Sung Kyung
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive sliding mode
Algorithms
Altitude
Attitude control
Attitudes
Back propagation
Back propagation networks
Backpropagation
Control methods
Control stability
Controllers
Disturbance observers
Dynamic models
Mathematical model
Neural networks
Nonlinear dynamical systems
quadrotor
Sliding mode control
Uncertainty
Unmanned aerial vehicles
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Summary:In this article, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. First, the dynamic model of the quadcopter is considered under external disturbances. Sliding mode controllers are then integrated with neural network algorithm to achieve the time-varying sliding surface; their coefficients in sliding surface are adjusted through backpropagation law. The disturbance observer is also combined with sliding mode controllers to estimate and handle the external disturbances. Finally, the Lyapunov theory is applied to validate the stability of suggested control method. The performance of proposed sliding mode control has been evaluated using a numerical simulation. The results show that the attitude and altitude controller based on suggested algorithm has a better tracking performance and disturbance rejection.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3064883