## 1 Introduction
In addition, a blur compensator is used to eliminate the chattering phenomenon caused by sliding mode control [21]. [PERSON] designs a fuzzy sliding mode controller to deal with the nonlinearity and uncertainty of the quadrotor UAV. This significantly improves the robustness, response speed and adaptive ability of the system, but the ability to cope with strong external disturbances needs to be further improved [22]. During the actual flight, external interference is unavoidable. The environment around the aircraft is complex and unpredictable, and the accuracy of the model is difficult to guarantee [23]. Neural networks have strong self-learning ability and can approximate nonlinear functions with ideal accuracy. Especially for the flight control system with high control precision, the neural network has the ability of better robustness and real-time performance [24]. Nonlinear terms and coupling terms will affect the control effect of quadrotor UAV. Therefore, [PERSON] proposes the method of artificial neural network direct inverse control to overcome the limitations of traditional control methods [25]. At present, a hybrid neural network controller is performed by combining other control methods with neural network. In reference [26], a method combining the neural network with fuzzy control is proposed, which is applied to the take-off and landing control for quadrotor UAV, so that it can overcome various external disturbances and maintain stability. [PERSON] designed an online adaptive neural network controller with different learning rates to ensure the control stability with a reliable anti-interference ability. According to all the results, combining the proposed optimal path planning scheme with a neural network controller in a flight environment with uncertainties, an energy-efficient UAV surveillance system with fast disturbance rejection response can be realized [27]. To train the fuzzy neural network controller of the aircraft, [PERSON] combines the adaptive law with the sliding mode theory to simulate the controller under the condition of time-varying wind. The results show that the steady-state error of the combined controller adjusted by the sliding mode theory is much smaller, and the control effect is significantly improved than the traditional controller [28]. In reference [29], a robust hybrid control system is designed, which includes a linear strictly negative imaginary (SNI) controller and an adaptive nonlinear neuro-fuzzy control law. Parameter changes such as nonlinear aerodynamic models and external disturbances are used to verify the effectiveness and improving the transient performance, system response and robustness for the quadrotor controller. Through the above research, it is found that feedback linearization and intelligent control methods can achieve ideal results for the control of UAV. Most of the past research on resistance disturbances of quadrotor UAV were based on the gust, which is a discrete or definite wind speed change. In this paper, the atmospheric turbulence sequence is established by Dryden model, and the continuous random wind is generated by the shaping filter, which can more truly simulate the air disturbance. As far as we know, this is the first time to use the dynamic inversion method of fuzzy neural network to deal with the influence of atmospheric turbulence on quadrotor UAV. In addition, remarkable results were achieved. The specific arrangement of the work is as follows: Section 2 analyzes the external wind field disturbance and establishes the atmospheric turbulent wind field sequence. According to the force balance and moment balance equations, Section 3 establishes the nonlinear dynamic equation of UAV under the condition of wind disturbance. Section 4 designs the fuzzy neural network dynamic inverse controller. The dynamic inverse control is used to eliminate the nonlinearity of UAV system, and the fuzzy neural network is used to compensate the inverse error caused by the external disturbance and the inaccuracy of the model.