[PERSON] [20] proposed a Model Predictive Control (MPC)-based strategy to address the control problem of Unmanned Aerial Vehicles (UAVs) carrying suspended loads. The study established a nonlinear dynamic model of the system using the Euler-Lagrange method and then linearized it to design the MPC controller. This controller simultaneously considers the UAV's translational and rotational dynamics in three-dimensional space, as well as the swinging dynamics of the suspended load. Through constrained optimization, it achieves a fast and stable balance with minimal swing angles. Numerical simulations verified the effectiveness of the controller, demonstrating superior performance compared to nonlinear control methods presented in previous literature. [PERSON] [21] proposed a hierarchical control strategy that combines Energy-Based Control (EBC) with a nonlinear state feedback controller based on Linear Matrix Inequalities (LMIs) to address the control problem of Unmanned Aerial Vehicles (UAVs) carrying suspended loads. The study established a nonlinear dynamic model of the system using the Euler-Lagrange method and designed two controllers: the EBC is used to stabilize the UAV's translational dynamics and suppress load swing, while the LMI-based controller is applied to manage the UAV's rotational dynamics. The innovation of this approach lies in its ability to avoid solving complex partial differential equations and its capability to handle large Lipschitz constants. [PERSON] [22] proposed a sliding-mode attitude control method for quadrotor UAVs based on a Nonlinear Expanded State Observer (NESO) and a Low-Frequency Disturbance compensator (LFDC), aiming to address the complex aerodynamic characteristics and susceptibility to interference in UAV attitude control. By combining NESO and LFDC in the inner loop, the author could more accurately estimate and compensate for the low-frequency components of the total disturbance. The outer loop used a nonsingular fast terminal sliding-mode controller with a Tracking Differentiator (TD) and variable switching gain, effectively compensating for the errors in the virtual disturbance estimate and the high-frequency components of the total disturbance, thereby enhancing the system's anti-interference capability and response speed. Simulation results showed that this method outperformed traditional control methods in terms of anti-interference and attitude-tracking performance. However, this method lacked consideration for unmodeled external disturbances, such as random airflow disturbances, which might affect the stability and tracking precision of the controller. By analyzing the shortcomings of the aforementioned reference articles, this paper addresses the technical challenge of achieving stable control for Unmanned Aerial Vehicles (UAVs) operating in complex nonlinear systems and under random airflow disturbances. A hybrid control algorithm is proposed, integrating Active Disturbance Rejection Control (ADRC) with Super-Twisting Sliding Mode Control (ST-SMC). This algorithm leverages the advantages of ADRC in disturbance estimation and compensation, as well as the strengths of ST-SMC in enhancing system robustness and suppressing high-frequency chattering, thereby achieving complementary improvements in control performance. The hybrid algorithm effectively mitigates external disturbances, reduces system oscillations, and significantly enhances the accuracy and stability of attitude and trajectory control. Simulation results demonstrate that the algorithm exhibits strong disturbance rejection and fast convergence during the dynamic response phase. Even under strong airflow disturbances, the UAV can quickly return to the desired trajectory. To better illustrate the proposed approach, the paper is divided into five sections. The first section establishes the UAV model. The second section presents the improved control algorithm, which adopts a fusion strategy of ADRC and ST-SMC to maintain stable flight and attitude under external disturbances.