robust intelligent trajectory tracking for quadrotors: integrating backstepping and feedback-error-learning techniques

This paper presents a novel hybrid control approach for quadrotor unmanned aerial vehicles (uavs), combining the lyapunov-based backstepping method for position control with a neural network-based feedback-error-learning (fel) technique for attitude control. the proposed strategy marks the first implementation of such a hybrid approach on a quadrotor platform, aiming to enhance control performance by leveraging classical and learning-based methods. the study's primary objectives include implementing a backstepping controller for altitude and horizontal position control, utilizing the fel technique for roll, pitch, and yaw angle control, and evaluating the hybrid method's effectiveness in improving trajectory tracking accuracy under various simulated uncertainties. simulation results demonstrate the proposed method's superior performance in trajectory tracking, with metrics such as root mean square error (rmse) and mean absolute error (mae) indicating enhanced accuracy. the research contributes to aerial robotics by showcasing the feasibility and effectiveness of integrating robust and adaptive control techniques, offering potential improvements in quadrotor performance within complex and uncertain environments.