Research on the Stability of UAV Attitude Under Hybrid Control Integrating Active Disturbance Rejection Control and Super-Twisting Sliding Mode Control  
[PERSON] 1  
[PERSON] 2  
[PERSON] 2  
addressing the inherent complexities of quadrotor dynamics--characterized by nonlinear dynamics, strong coupling effects, and under-actuation characteristics--offering enhanced theoretical foundations for precision control implementations. To investigate the impact of external disturbances on UAVs, the main current control methods include PID [1; 2; 3; 4; 5; 6], backstepping [7; 8; 9; 10; 11], and sliding-mode control [4; 5; 6; 7; 8; 9; 10; 11]. [PERSON] et al. [15] introduced a particle swarm optimization algorithm to adjust the parameters of the PID controller, optimizing system performance. Simulation results indicated that their optimization method was effective and reasonable. [PERSON] [16] designed a dual-loop controller based on the PID algorithm for position and attitude control, respectively, after establishing the dynamics model of the quadrotor UAV. Flight experiments verified the reliability and effectiveness of this design. In recent years, research on PID control has also included its integration with other control methods to enhance system adaptability and control precision. For instance, by combining fuzzy control or neural network technology, PID control can achieve adaptive parameter adjustment in dynamic environments, thereby better coping with flight missions under complex disturbance conditions. These improvements have expanded the application scenarios of PID control, enabling it to play a greater role in the precise control of quadrotor UAVs. [PERSON] [17] designed an attitude and position controller for quadrotor UAVs based on the backstepping method. Simulation results indicated that this approach exhibited strong adaptability in addressing the uncertainty associated with load variations and could effectively suppress external airflow disturbances, thereby enhancing the stability and robustness of the aircraft. However, the method also had limitations. For instance, it was sensitive to initial conditions. If the initial conditions deviate from the expected range, the performance of the controller might be affected. [PERSON] [18] proposed an adaptive fault-tolerant controller based on second-order fast nonsingular terminal sliding-mode control to ensure flight stability and smooth landing in the event of a single-rotor failure in quadrotor UAVs. The author effectively eliminated chattering in the control output through the second-order sliding-mode control method while maintaining rapid response and high-precision trajectory-tracking capabilities. Moreover, the author discussed the impact of the rotation axis and the norm of the angular velocity on dynamic tracking performance and proposed improvement strategies, especially for performance enhancement in cases of a large moment of inertia. Experiments verified the feasibility and tracking performance of the controller. However, since this controller relied on the optimization of multiple parameters, the selection of these parameters required a trade-off between rapid response and precise tracking. This process was extremely time-consuming and complex, posing high demands on the computational capacity of the processor. [PERSON] [19] analyzed the closed-loop control system of UAVs and used the non-parametric prediction error estimation method to identify the actuator module, proving that the identified model was unbiased compared to the actual model. A safety controller was designed based on the identified model, and the connotation of UAV safety was elucidated. However, the paper lacked an in-depth discussion on the principle and applicable scenarios of the non-parametric prediction error estimation method and did not consider the impact of various complex environmental factors on the performance of the controller.