## 1 Introduction  
In the past few decades, the Unmanned Aerial Vehicle (UAV) has been greatly popularized in civil fields such as logistics and geological exploration [1, 2]. Most of the control systems for UAVs are traditional controllers, such as PID controllers. However, due to the nonlinear characteristics of the real aircraft aerodynamic model, the control performance of the traditional methods cannot fully meet the expectations and their robustness behaves badly, which also brings secondary problems such as the long control system designing process. In order to overcome this problem, in recent years, a large number of studies have focused on the design of nonlinear control systems, such as adaptive control [3, 4], intelligent control [5, 6], and so on. Representative among these studies is the Learn-to-Fly project conducted by NASA's Langley Research Center [7, 8]. Accurate acquisition of real-time dynamic information for aircrafts is the premise of nonlinear adaptive control adjustment. Aerodynamic identification can play a role in the whole life cycle of an aircraft. There are many different application scenarios, including aircraft offline aerodynamic model correction, aircraft adaptive controller design, and post-damage control reconstruction, etc. In some application scenarios, the aerodynamic identification must be carried out online, and the flight data collected by the sensor are obtained step by step, and finally, the accurate identification results are obtained. The method of aircraft dynamics identification includes two categories: time domain identification method and frequency domain identification method. The regression method representedby the least square method is a kind of commonly used basic method, more methods have been created based on the least square method. Ref. [9] combined the general least square method with the support vector machine (SVM) to achieve the predicted stable hysteresis loop. Ref. [10] introduced a method of Smoothed Partitioning with Localized Trees in Real Time (SPLITR), which uses a local model network for automatic real-time global aerodynamic modeling. In addition, the commonly used identification methods include maximum likelihood method [11; 12], Kalman filter method [13; 14] and so on. As for the frequency domain, ref. [15] proposed a method to estimate the parameters of the linear model in real time by using the equation error of the recursive Fourier transform in the frequency domain. [PERSON]. [16] established an equation error method in the frequency domain to estimate the dimensionless stability and control derivative in real time and had data forgetting, fusion prior information, and optimization excitation added. [PERSON]. [17] introduced orthogonal optimized multi-sine inputs to excite aircraft dynamic response. Intelligent methods are also gradually being used in aerodynamic identification. [PERSON]. [18] used an adaptive neuro-fuzzy inference system extended by the Delta method (ANFIS-Delta) based on a feedforward neural network to estimate aerodynamic parameters to enhance the principle of an adaptive neuro-fuzzy inference system (ANFIS) in the Delta method. Ref. [19] applied a neural network to model the dynamic process of a canard-wing aircraft. In recent years, there have been many attempts to apply neural networks to aerodynamic identification [20; 21; 22; 23]. In terms of nonlinear adaptive control methods, there are many different technical routes. For example, the series control system combined with a neural network or fuzzy controller and PID controller realizes online adjustment of control gain based on control error or other observations. Nonlinear dynamic inversion (NDI) control is also an adaptive control method which has strong adaptability and versatility. NDI control can better cope with the complicated decoupling work among different loops of nonlinear objects and does not need to repeatedly adjust the gain of each loop; it also has a great number of applications in aircraft nonlinear control in the long course of development.