Anti-Disturbance Trajectory Tracking Control for Quadrotor UAVs Based on Radial Basis Function Neural Network and Integral Terminal Sliding Mode Control

Quadrotor unmanned aerial vehicles (UAVs) operating in complex and dynamic environments, especially when subjected to unknown disturbances such as wind, can experience significant degradation in the stability of trajectory tracking control. Current research on UAV control has proposed algorithms that exhibit good disturbance rejection capabilities for small and weak disturbances, but their effectiveness decreases significantly as the disturbance magnitude increases. To address this issue, this paper proposes a hybrid control strategy that combines a Radial Basis Function Neural Network (RBFNN) with Integral Terminal Sliding Mode Control (ITSMC). The RBFNN is designed as an online disturbance observer, capable of estimating and compensating external disturbance forces and torques in real time, with an adaptive weight law. The ITSMC utilizes an integral term to eliminate steady-state errors and a terminal sliding mode term to achieve finite-time convergence of tracking errors. Simulation results demonstrate that the proposed controller maintains high-precision trajectory tracking and attitude control performance under various disturbance conditions, exhibiting strong robustness and anti-disturbance capability, and outperforms other controllers in overall performance.