## 2 Method
### Maneuverability Model  
The maneuverability model is a regression model that aims to build a bridge between the desired acceleration and the control instructions while taking into account the influences of the UAV's current state and external environmental factors. The higher the speed, the greater the drag on the UAV. Acceleration has a non-linear relationship with velocity influence. Due to an insufficient energy supply, the motor rotation speed will decrease under the same control instruction. Then, the input-output relationship of the maneuverability model on the constructed control platform was proposed to reflect the influence of these factors (Figure 1). Since the wind speed changes rapidly in the actual flight environment, it is difficult to obtain the relationship between UAV acceleration and fixed wind speed. The flight experiments were conducted under the condition of almost no wind, and the maneuverability model does not take wind speed into account. The input of the model includes the actual UAV velocity (including velocity in the x-direction and in the y-direction), the desired acceleration (including acceleration in the x-direction and in the y-direction), and the battery power supply. The output of the model is the control instructions, which include ROLL, PIT, YAW, and THR. The regression prediction model is mainly divided into linear regression and nonlinear regression. In actual flight control, there is no linear relationship between UAV speed, power voltage, and air resistance. Then, a nonlinear regression model was adopted to build a bridge between the UAV state and instructions. At present, artificial neural networks (Back Propagation (BP), Recurrent Neural Networks (RNNs), etc.) are widely used to solve the nonlinear regression problem. The BP or RNN model does not consider the temporal relationship between variables, or cannot satisfy the temporal and spatial consistency requirement of the maneuverability model. They can only fuse information in a shorter time window. Then, the LSTM model was proposed to optimize this problem. In the flight experiment of the UAV, the acceleration is easily affected by the environment, and the information in one period will affect the flight state of the UAV. Based on the current situation and acceleration requirements, the output of the model (the desired instructions) depends on the period of the information segments; thus, RNN-LSTM was selected. The purpose of the RNN-LSTM model is to better store and retrieve previous information and to better extract long-distance dependencies with the data to improve the shortcomings of the RNN model. The key idea of RNN-LSTM is that the final training result is obtained by combining LSTM memory cells. The LSTM maintains an uninterrupted error flow through the input and feedback, avoiding the long stagnation that forms among them. It can avoid the problem of gradient diffusion and gradient explosion. The LSTM units can store information for long periods of time and exploit long-distance dependencies in the data. The access to this memory unit is controlled by some special logical unit doors, which contain the functions of data storage, writing, and reading. These logic units are required to implement the correct value for each gate and select the gate function by different permissions. The flow chart of the UAV maneuverability model on the RNN-LSTM algorithm is shown in Figure 3. In the input layer, the input data sequences of the UAV, which are the voltage, velocity, and acceleration of the UAV during the flight, are defined. There are five layers in the hidden layer. \(h_{n-1}\) and \(c_{n-1}\) are the output and state of the middle LSTM memory cell, respectively. In the training layer, the actual loss function value is calculated according to the theoretical output and the model output, and then continuously fed back to the hidden layer. The loss function used is the root mean square (RMS) error function.