## 3 Control Methods Used in Agricultural Vehicle Path Tracking Control
### Dry Field Agricultural Machinery
However, influenced by complex environments and uncertain external disturbances, when the tractor speed increases, the defects such as nonlinearity, hysteresis, and instability of the tractor-field path system become more prominent, which will affect system performance. Combined with optimal control theory, [PERSON] et al. [43] designed a dual-parameter optimal control algorithm based on speed and steering angle. [PERSON] et al. [44] designed a dual objective joint sliding mode control algorithm to solve the influence of tractor speed on the performance of the navigation control system. The field test platform of the tractor's automatic navigation control system is shown in Figure 7. This algorithm achieves adaptive adjustment of speed while ensuring stable straight-line path tracking. The pure pursuit control method has the characteristics of simulating human driving behavior with few control parameters, but in agricultural scenes, the robustness of preview point selection is poor and cannot be adjusted adaptively. In the study of pure pursuit model look-ahead distance, dynamic look-ahead distance performs better in path tracking than fixed look-ahead distance, although better tracking accuracy can be obtained when the look-ahead distance is equal to the wheel distance. To address the problem of poor adaptability of traditional pure pursuit algorithms to speed changes, [PERSON] [45] proposed an improved pure pursuit algorithm on the basis of a seeker optimization algorithm (SOA), which can calculate the look-ahead distance online using the fitness function. The improved pure pursuit model algorithm can effectively resist the influence of speed changes. [PERSON] et al. [46] used the Dongfanghong 1104-C tractor as the test platform to determine  
Figure 7: Tractor automatic navigation control system test platform. the look-ahead distance in the pure pursuit model in real-time through particle swarm optimization (PSO). [PERSON] et al. [47] and [PERSON] et al. [48] developed a path-tracking method for agricultural machinery on the basis of the fuzzy adaptive pure pursuit model, which realized online real-time adjustment of look-ahead distance. The developed navigation controller has the advantages of short rise time and small overshot. [PERSON] et al. [48] developed the automatic navigation operation system of a crawler-type rape seeder, as shown in Figure 8. [PERSON] et al. [49] used deep reinforcement learning (DRL) to improve the pure pursuit model algorithm and derive correction commands to reduce tracking errors due to the improper selection of preview points. [PERSON] et al. [50] designed a path-tracking algorithm based on the optimal target point, which searched for the optimal target point in a forward-looking area using an evaluation function. The quality and efficiency of manual harvesting operations largely depend on the driver's driving skills. Applying automatic navigation technology to grain crop harvesting will help improve the quality, efficiency, and intelligence level of combined harvesters. To improve the quality of corn harvesting, [PERSON] et al. [51] developed an automatic tracking system for the corn harvester, which controlled the harvester by a pure pursuit model algorithm and dynamically adjusted the look-ahead distance by fuzzy logic. In response to the problem of missing cuts during the straight-line tracking harvesting operation of a combine harvester, [PERSON] et al. [52] developed a single-neuron PID navigation controller for combine harvesters. Single-neuron PID can achieve rapid adjustment of control parameters through self-learning, which has the characteristics of small overshoot and fast entry into a steady state. The assisted driving system of a combined harvester with intermediate angle error can lead to path tracking deviation, which will limit the efficiency of the harvesting operation. [PERSON] et al. [53] proposed an improved path-tracking controller with mid-angle adaptive calibration. [PERSON] et al. [54] developed a path-tracking PD controller for peanut harvesters.