## 5 Conclusions  
Path tracking control is one of the hotspots in the research of agricultural vehicles' automatic navigation. Researchers use position and attitude sensors, vehicle kinematics models, and dynamics models to obtain position and heading errors and develop path-tracking controllers to control agricultural vehicles to work along reference paths. This paper reviews and summarizes the latest research status on path-tracking control of autonomous agricultural vehicles, including vehicle modeling and the development of path-tracking control methods. In terms of vehicle modeling, we reviewed and summarized the kinematic model and dynamic model of agricultural vehicles. The kinematic model includes the bicycle model (simplified four-wheel agricultural machinery kinematic model), the agricultural tracked vehicle kinematic model, and the agricultural articulated vehicle kinematic model. The kinematic model does not consider the dynamic parameters of the vehicle, so the calculation burden is small, and the structure is simple, but ideal assumptions often introduce errors. The kinematic model tends to achieve better tracking accuracy and effect under low-speed conditions. The dynamic model is beneficial for improving the robustness of control systems, but dynamic parameters are difficult to obtain online, and model accuracy is difficult to guarantee. Undoubtedly, establishing an accurate dynamic model is beneficial for improving the path-tracking accuracy of vehicles, and many researchers are currently working in this area.  
In the aspect of controller development, most controllers are based on the vehicle kinematic model, and only a few controllers consider the dynamic characteristics of agricultural vehicles in the field. According to actual working conditions, researchers used GA, PSO, DRL algorithm, etc., to improve and optimize the traditional path tracking algorithm so as to improve the robustness and accuracy of the controller. Single neurons, PSO, and DRL algorithms are used to realize the tuning of PID control parameters. The look-ahead distance of the pure pursuit model has a significant impact on the path-tracking accuracy of vehicles. Existing research has adopted PSO, fuzzy algorithm, DRL, and SOA to improve the pure pursuit model to achieve dynamic adjustment of look-ahead distance. Sliding mode control has the characteristic of being insensitive to external disturbances, but there is a problem with chattering. Researchers have developed SOSMC and sliding mode controllers with disturbance observers to suppress chattering. The linear model predictive control has better performance than the nonlinear model predictive control, but the linear model predictive control has the problem of weakening the controller's ability to respond to reference path curvature and heading changes. The improvement of the model predictive control algorithm should be strengthened.  
With the innovation of control theory and agricultural machinery development, intelligent controllers may provide better solutions for agricultural vehicle path tracking. In conclusion, it is hoped that this paper will provide sufficient background and understanding for path tracking control of autonomous agricultural vehicles.  
Conceptualization, Q.W. and J.H.; writing--original draft preparation, Q.W.; writing--review and editing, C.L., C.W., H.L. ([PERSON]), H.Y., H.L. ([PERSON]) and Z.W.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2022 YFD1500704).  
Not applicable.  
This research was supported by the 2115 Talent Development Program of China Agricultural University.  
The authors declare no conflict of interest.