## 3 Results
By observing the trajectory tracking position simulation results of the UAV in Figure 6, it can be concluded that the A-ST-SMC algorithm outperforms traditional PID and ADRC control algorithms in terms of dynamic response, disturbance rejection performance, and steady-state tracking error. This also indicates that the proposed control algorithm consistently demonstrates high-precision tracking in the position direction under random airflow disturbance conditions. The curve shown in Figure 9a demonstrates the dynamic response of the UAV in the roll angle direction under random airflow disturbance conditions. For convenience of analysis, the UAV's dynamic response is divided into two stages: the initial stage from 0 to 5 s and the steady-state stage after 5 s. In the initial stage, when the random airflow disturbance is introduced, the PID and ADRC control algorithm curves show significant fluctuations, and after being disturbed by random airflow, there is a notable deviation that takes a long time to recover stability. In contrast, the A-ST-SMC shows better disturbance rejection performance. From the curve, it can be observed that with the introduction of random airflow, the A-ST-SMC curve hardly exhibits overshoot or long-lasting jitter, and during the steady-state phase, the curve quickly converges to zero. Figure 9b shows the dynamic response curve of the UAV in the pitch angle direction. From the curve, it can be observed that in the initial stage, due to the random airflow disturbance, PID, ADRC, and A-ST-SMC all exhibit significant fluctuations. However, upon examining the curve, it is apparent that the fluctuations in the PID and ADRC control algorithms, both in terms of amplitude and duration, are significantly larger than those of the A-ST-SMC control algorithm. Especially after the random airflow disturbance disappears, the output curve of A-ST-SMC always recovers to a stable state, while PID and ADRC take relatively longer to converge. Similarly, Figure 9c shows the dynamic response curve of the UAV in the yaw angle direction. In the initial stage, from 0 to 5 s, due to the random airflow disturbance introduced into the system, the PID, ADRC, and A-ST-SMC curves all show considerable jitter and prolonged fluctuations. Particularly, PID and ADRC show more frequent and larger fluctuations than A-ST-SMC. In the steady-state phase, A-ST-SMC can quickly recover to a stable state after the random airflow disturbance disappears, while PID and ADRC require a relatively longer time to stabilize. From the comparison, it can be seen that PID and ADRC, whether in terms of overshoot magnitude, fluctuation frequency, or the time required to recover to a steady state, are significantly worse than A-ST-SMC. This also indicates that the A-ST-SMC demonstrates greater robustness and superiority under random airflow disturbance conditions. From the analysis of the above curves, it can be concluded that the A-ST-SMC algorithm significantly outperforms traditional PID and ADRC control algorithms in terms of dynamic response, disturbance rejection performance, and steady-state control accuracy in the roll, pitch, and yaw directions. It also demonstrates that this algorithm can achieve faster convergence speeds and higher precision when dealing with complex disturbances, showing faster response times and better stability. Furthermore, it has significant advantages in disturbance rejection ability and precise steady-state performance, fully proving the applicability and superiority of this algorithm in complex environments such as airflow disturbances. The curves shown in Figure 10a-c represent the position tracking error curves of the UAV on the X, Y, and Z axes, respectively, in order to verify the self-balancing control of the UAV under random airflow disturbance conditions using the control algorithm proposed in this paper.