Regular Paper

International Journal of Control, Automation and Systems 2019; 17(9): 2365-2374

Published online July 4, 2019

https://doi.org/10.1007/s12555-018-0720-7

© The International Journal of Control, Automation, and Systems

Neural Network-based Robust Adaptive Certainty Equivalent Controller for Quadrotor UAV with Unknown Disturbances

Oualid Doukhi and Deok Jin Lee*

Kunsan National University

Abstract

In this paper, a robust adaptive neural network certainty equivalent controller for a quadrotor unmanned aerial vehicle is proposed, which is applied in the outer loop for position control to directly generate the desired roll and pitch angles commands and then to the inner loop for attitude control. The newly proposed controller takes into account the vehicle’s kinematic and modelling error uncertainties which are associated with external disturbances, inertia, mass, and nonlinear aerodynamic forces and moments. The control method integrates an adaptive radial basis function neural networks to approximate the unknown nonlinear dynamics with certainty equivalent control technique, in this way leading to the fact that precise dynamic model and prior information of disturbances are not needed. The adaptation law was derived by using a Lyapunov theory to verify the stability and superiority of the new algorithms. The performance and effectiveness are also verified by carrying out several simulations. It was shown from the analysis that the altitude, position, and attitude tracking errors are converged to zero and the closed loop stability is guaranteed under extreme conditions.

Keywords Adaptive control, artificial neural network ANN, PID, quadrotor UAV, trajectory tracking.

Article

Regular Paper

International Journal of Control, Automation and Systems 2019; 17(9): 2365-2374

Published online September 1, 2019 https://doi.org/10.1007/s12555-018-0720-7

Copyright © The International Journal of Control, Automation, and Systems.

Neural Network-based Robust Adaptive Certainty Equivalent Controller for Quadrotor UAV with Unknown Disturbances

Oualid Doukhi and Deok Jin Lee*

Kunsan National University

Abstract

In this paper, a robust adaptive neural network certainty equivalent controller for a quadrotor unmanned aerial vehicle is proposed, which is applied in the outer loop for position control to directly generate the desired roll and pitch angles commands and then to the inner loop for attitude control. The newly proposed controller takes into account the vehicle’s kinematic and modelling error uncertainties which are associated with external disturbances, inertia, mass, and nonlinear aerodynamic forces and moments. The control method integrates an adaptive radial basis function neural networks to approximate the unknown nonlinear dynamics with certainty equivalent control technique, in this way leading to the fact that precise dynamic model and prior information of disturbances are not needed. The adaptation law was derived by using a Lyapunov theory to verify the stability and superiority of the new algorithms. The performance and effectiveness are also verified by carrying out several simulations. It was shown from the analysis that the altitude, position, and attitude tracking errors are converged to zero and the closed loop stability is guaranteed under extreme conditions.

Keywords: Adaptive control, artificial neural network ANN, PID, quadrotor UAV, trajectory tracking.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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