## 1 Introduction  
The quadrotor UAV can adjust the attitude arbitrarily and has the ability to move at high speed and turn quickly [1; 2]. Compared with fixed-wing UAVs, it has obvious advantages in maneuverability and flexibility, which makes it much more suitable for performing high-risk tasks in confined spaces without humans [3; 4]. When drones spray pesticides over farms or deliver express delivery in human communities, the design of the controllers is mainly determined by the load problem [5; 6; 7]. If the search and the rescue work is carried out over a canyon or sea level, not only the harsh environment but also strong winds and atmospheric turbulence should be considered [8; 9]. Generally speaking, the turbulent lies in the stern of a ship, which is generated by the heat transfer, wind shear and other reasons [10]. The randomness of atmospheric turbulence is strong, which seriously affects the flight effect of unmanned aerial vehicles. Due to the highly nonlinear and strong coupling of attitudes, external airflow interference during flight [11], the perturbation of aerodynamic parameters for the model, the accurate modeling for quadrotor UAV is considerably difficulty [12]. Under the strong external disturbance, the UAV cannot maintain the desired attitude, which often leads to the failure of the mission. This requires its control system to have strong robustness and anti-interference ability [13]. Linear control is one of the commonly used control methods for quadrotor UAV flight systems. Since the aircraft itself is a nonlinear system, the linearization method can used to obtain an approximate model, and thus reducing the coupling among attitudes. For example, the linearization methods of PID and LQR are both commonly used to improve the performance of flight control systems. Kaba improves the PID algorithm for quadrotor UAVto ensure the optimality of the controller by the convexity-based proxy firefly algorithm, and evaluates the performance under different convex combination values [14]. In the presence of wind and other environmental disturbances, LQR and LQR-PI controllers are employed to reduce the deviation of state trajectory. According to the state variables and preference factors of quadrotor aircraft, the weighting matrix can be automatically adjusted [15]. Since the model is not accurate enough and the controller design process is cumbersome, it is difficult to meet the high-quality requirements for quadrotor UAV. Therefore, the nonlinear control method, which can fully consider the nonlinearity of quadrotor UAV, can be utilized to gain better performance. Different from the approximate linearization, the feedback linearization is better method based on accurate model, which turns the nonlinear system into the linear one by the method of controller design. According to the incremental nonlinear dynamic inversion (INDI), a cascade control method is proposed for the attitude loop and position loop. Compared with the nonlinear calculation, the linearization method is used to calculate the effect of thrust vector increment [16]. Furthermore, the sliding mode control [17], the active disturbance rejection control [18] and the fuzzy control [19] are often used as control algorithms for quadrotor UAV. A dynamic surface-active disturbance rejection control strategy is proposed by [PERSON], in which a first-order filter is introduced to obtain the derivative of the virtual control and simplifies the control law of the system. Compared with the classical ADRC controller, the advantages and effectiveness of the developed control strategy are given in the simulations [20]. [PERSON] proposes a self-correcting adaptive PID control method to solve the problem of attitude and position stability of quadrotor UAV against the parameter uncertainty and external disturbance. By employing the sliding mode control as the adaptive mechanism, the retuning gain of the manual controller for the proportional-integral-derivative controller is overcome.