# Fractional-Order Sliding Mode with Active Disturbance Rejection Control for UAVs
## 5 Conclusions  
In this paper, the attitude controller was designed with the quadrotor attitude angle as the control object, and some series of simulation experiments were carried out. The main conclusions are as follows: (1) Finite-time convergence. By adopting the FOSM-ADRC method, the designed quadrotor unmanned aerial vehicle (UAV) attitude controller successfully solves the problem of the convergence of the control variables in a finite time. This was verified by simulations in the Matlab/Simulink (version:R2022b) environment, showing that the controller was able to respond quickly and reach a steady state within a predetermined time. (2) High-frequency vibration suppression. The simulation results further confirm that the controller can effectively suppress the high-frequency vibration of the output. This is critical for improving system stability and reducing errors caused by vibration, especially when operating at high speeds or in complex environments. (3) Anti-interference capability. The designed controller exhibits enhanced anti-interference capability compared to ordinary sliding mode control (SMC) and PID controllers. This enhanced robustness allows the controller to maintain performance stability and reliability in the face of external perturbations and system uncertainties. (4) Comprehensive efficacy. In summary, the simulation results show that the controller not only successfully suppresses the high-frequency jitter in the output of the common sliding mode controller but also achieves strong anti-interference capability. These characteristics make the FOSM-ADRC method have significant application potential and advantages in the field of quadrotor UAV attitude control. Future research can further explore the performance of the FOSM-ADRC method under different types of UAVs and a wider range of operating conditions, as well as explore how to further optimize the controller parameters in order to improve the system's responsiveness and stability, e.g., by using intelligent algorithms to automatically regulate  
Figure 8: Experimental results of PID and FOSM-ADRC with strong interference.  
the parameters or by reducing the regulated parameters while guaranteeing the control performance, among other aspects.  
Conceptualization, Z.Z. and H.Z.; Methodology, Z.Z.; Software, Z.Z.; Validation, Z.Z.; Formal analysis, Z.Z.; Investigation, Z.Z.; Resources, H.Z.; Data curation, Z.Z.; Writing--original draft, Z.Z.; Writing--review & editing, H.Z.; Supervision, H.Z.; Project administration, H.Z.; Funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the independent research key project of the Center for Applied Mathematics Inner Mongolia, \"Research on mathematical models in remote sensing monitoring of typical ecological environment problems in the Inner Mongolia section of the Yellow River Basin\" (No. ZZYJZD2022001).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.