On the Development of a Data-Driven-Based Fractional-Order Controller for Unmanned Aerial Vehicles

Abstract

Proper control is necessary for ensuring that UAVs successfully navigate their surroundings and accomplish their intended tasks. Undoubtedly, a perfect control technique can significantly improve the performance and reliability of UAVs in a wide range of applications. Motivated by this, in the current paper, a new data-driven-based fractional-order control technique is proposed to address this issue and enable UAVs to track desired trajectories despite the presence of external disturbances and uncertainties. The control approach combines a deep neural network with a robust fractional-order controller to estimate uncertainties and minimize the impact of unknown disturbances. The design procedure for the controller is outlined in the paper. To evaluate the proposed technique, numerical simulations are performed for two different desired paths. The results show that the control method performs well in the presence of dynamic uncertainties and control input constraints, making it a promising approach for enabling UAVs to track desired trajectories in challenging environments.