## 3 Results
Random airflow disturbance is introduced between 0 and 5 s, and the output curves of the traditional control algorithms, PID, ADRC, and A-ST-SMC algorithm, are observed to analyze the response performance of the three algorithms to random airflow disturbances. By observing the curves, it can be seen that in the disturbance phase, the error in the output curves of the traditional control algorithms shows a more significant jitter. Although the magnitude of the jitter is relatively small, the time for the magnitude to decrease is quite  
Figure 9: Experimental results of UAV attitude angles. long. In contrast to the traditional control algorithms, the output curves of the algorithm proposed in this paper show a more impressive performance. By observing the curves, it is evident that A-ST-SMC quickly recovers to a steady state, and its jitter magnitude is lower than that of ADRC and PID. From the analysis of the UAV position tracking error curves, the following conclusions can be drawn: The A-ST-SMC algorithm significantly outperforms traditional control algorithms in terms of error tracking on the X, Y, and Z axes, as well as the fast convergence  
Figure 10: Experimental results of UAV position tracking errors. during the stable phase after the disturbance. This further reflects the disturbance rejection performance of the algorithm under random airflow disturbances. Figure 11a-c show the error-tracking curves of the UAV's attitude angles in the roll, pitch, and yaw directions, respectively. Similarly, random airflow disturbance is introduced between 0 and 5 s, and the steady-state performance of the three algorithms in terms of attitude angle direction is observed. Figure 11: Experimental results of UAV attitude errors. By observing the roll angle error curve in Figure 10(a), it can be seen that traditional control algorithms always exhibit large overshoot disturbances during the disturbance phase, and the curves show long jitter times. However, although A-ST-SMC exhibits an overshoot momentarily, the controller adjusts to reduce the overshoot each time it occurs during subsequent oscillations, and the jitter magnitude is clearly lower than that of the PID and ADRC control algorithms. Similarly, by observing the yaw angle error curve in Figure 10(c), it can be seen that both PID and ADRC not only show large overshoots but also experience significantly more oscillations. However, A-ST-SMC performs better in terms of both error peak values and convergence speed compared to the traditional control algorithms. From the above analysis, it can be concluded that in the UAV's attitude angle error tracking curves, the A-ST-SMC algorithm significantly outperforms traditional control algorithms in terms of robustness, error tracking ability, and response speed. This further proves the tracking performance and adaptability of the algorithm under airflow disturbances. Figure 12 shows the experimental results of the UAV's attitude angular velocity. From the curves in the figure, it can be observed that after encountering airflow, the UAV adjusts its attitude angular velocity to bring the UAV body to a balanced state. By comparing the traditional PID algorithm, ADRC algorithm, and the A-ST-SMC, it can be seen that under the adjustment of the fusion controller, the UAV quickly stabilizes its attitude. However, both PID and ADRC algorithms exhibit much larger overshoots and slower response speeds than the fusion controller. Table 3 compares the traditional PID algorithm, the ADRC algorithm, and the control method proposed in this study. The results indicate that the control algorithm presented in this paper offers significant advantages over traditional methods in terms of response amplitude and reaction time. This improvement is mainly attributed to the design of the integrated controller. However, this strategy also introduces a considerable computational burden, increasing the overall complexity of the algorithm. Nevertheless, the performance gains suggest that the added complexity is justified.