## Methods
### Design of Unmanned Agricultural Machine Operation under Improved Fuzzy Adaptive Algorithm
#### 3.1.1 Kinematic Model and Improved Adaptive Algorithm Design  
The automatic driving control algorithm of unmanned agricultural aircraft can be divided into two parts: path tracking and travelling speed regulation, in which the precise path tracking algorithm can ensure that the vehicle moves along the planned path accurately and correctly, avoiding the safety hazards caused by deviation from the route and adapting to the complex changes in the environment and terrain. The adjustment of the travelling speed can improve the work efficiency under the condition of mastering various dynamic information, effectively reduce fuel consumption, and guarantee the safety of travelling while taking into account the operating speed [24]. This study ignores the role of suspension and does not take into account the existence of the side-slip and lateral inclination of the vehicle and the influence of the steering system. Considering all practical factors can make the model very complex, thereby increasing the computational complexity and algorithm design difficulty. Therefore, in the design process, this study ignores the suspension effect and vehicle tilt influence to facilitate the calculation and analysis of the model, reduce uncertainty, and focus on factors related to motion control. The inclination of vehicles does indeed affect their driving conditions on different terrains, but the research mainly analyses the application of agricultural machinery, and the overall height difference of farmland terrain has a relatively small impact on vehicle inclination. At the same time, the auto drive system researched and designed can adjust the changes caused by the terrain through damping and other adjustment systems to ensure the stability of the operation. With the help of the two-wheeled vehicle model to achieve the establishment of the kinematic model of unmanned agricultural machinery, the direction of the coordinate system in the kinematics model is due east and due north, in which the counterclockwise direction of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature** & **Advantage** & **Shortcoming** \\ \hline \multirow{2}{*}{[PERSON] et al. [22]} & Improved the performance of PID control, & High sensitivity to changes in \\  & which helps to improve the accuracy of & environmental characteristics; more data \\  & trajectory control & are required for network training \\ \hline \multirow{2}{*}{
\begin{tabular}{c} [PERSON] \\ et al. [23] \\ \end{tabular} } & Improved attitude control performance & System complexity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._the heading angle is positive. The lateral deviations to the right and left of the path are distinguished as negative-positive. When the front wheel steering of agricultural machinery adopts the Ackermann steering mechanism, the rotation angles of the left and right front wheels are different during steering. Therefore, when studying the kinematic model of agricultural machinery, the kinematic model of agricultural machinery is simplified as a two-wheeled vehicle model. The front wheel angle sensor is fixed to the fixed rod of the steering mechanism, and the sensor rotation link is installed in the middle position of the steering mechanism rotation rod. The feedback of the front wheel angle information from this position is more accurate. The simplified kinematic equations of the model are easy to calculate and analyse, and without considering other factors, the model can reduce parameters, making system analysis and algorithm control more efficient, and can describe the planar motion state of agricultural machinery well [25]. The model is shown schematically in Figure 1.  
In Figure 1, \(\zeta\) represents the front wheel angle, \(\gamma\) represents the steering angle deviation, and \(\theta\) represents the heading angle. The heading angular velocity in this model can be expressed as Equation (1).