## 6 Conclusions  
The flight stability of an aircraft may be adversely influenced by external disturbances and actuator faults. When performing tasks in different environments, the aircraft is considerably affected by the climate, including temperature, humidity, and air pressure. Aircraft collision may also happen in unknown flight environments. All of these adverse factors adversely impact the actuator or sensors and induce uncertain changes in the aircraft's model parameters. To address the above problems, we integrated an ADRC controller with a spatio-temporal RBF neural network to construct an FTAC scheme. This RBF neural network was capable of the online estimation of uncertain terms in the system model. In addition, the optimal gradient descent method reduced the controller's calculation amount and achieved a faster response. A combination with the sliding-mode controller further improved the attitude stability of the aircraft. The experimental results showed that the FTAC can maintain stable flight in finite time, thus verifiying the reliability of the proposed FTAC. Therefore, the method proposed in this paper can be used to control a multi-rotor UAV with actuator failure, gust disturbance, and model uncertainty. Furthermore, the experimental data showed that in the case of increasing actuator fault values, the system's estimates of parameters due to model uncertainty continually increased, which indirectly led to a decrease in the controller's tracking performance. Due to the loss of the actuator power caused by the large fault value and the different minimum lift required for different masses of aircraft, the control effect of the controller on different control objects was not identical. Therefore, the fault-tolerant controller proposed in this paper does not fully satisfy all the use scenarios. As a result, certain improvements are still needed in this area.  
Conceptualization, L.H., J.Z., D.L. and X.X.; methodology, L.H., J.Z., D.L. and X.X.; validation, L.H., D.L. and J.Z.; formal analysis, L.H. and D.L.; investigation, L.H.; resources, L.H.; data curation, L.H. and D.L.; writing--original draft preparation, L.H.; writing--review and editing, D.L.; visualization, X.X.; supervision, J.Z.; project administration, L.H.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Interdisciplinary Project of Yangzhou University Crop Science Special Zone, grant number yzuxk202007; and the Natural Science Research Project of Universities in Jiangsu Province (20 KJB470032).  
Data Availability Statement: Data sharing not applicable.  
The authors would like to thank the Crop Science Special Zone Interdisciplinary Program of Yangzhou University for financial support and the Advanced Vehicle Health Management Laboratory of Nanjing University of Aeronautics and Astronautics for equipment support.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADRC & Active Disturbance Rejection Control \\ UAV & Unmanned Aerial Vehicles \\ FTAC & Fault-Tolerant Aircraft Control \\ RBF & Radial Basis Function \\ FTC & Fault-Tolerant Control \\ DC & Direct-Current \\ ESO & Extended State Observer \\ SMNLSEF & Sliding Mode Nonlinear State Error Feedback \\ ST-RBFNN & Spatio-Temporal Radial Basis Function Neural Network \\ \end{tabular}