## 1 Introduction  
Automatic navigation technology for intelligent agricultural machinery is one of the most important support technologies for modern agricultural equipment [1] that guarantees a high level of agricultural production. Path tracking is a key technology of automatic navigation. A pure pursuit model is a geometric method used to simulate a driver's driving behavior in which the steering radius is calculated in real time by its own posture information and a preset look-ahead distance without relying on a vehicle dynamics model [2, 3]. It is widely used in unmanned agricultural machinery path tracking and path control. In narrow environments, such as greenhouses, intelligent agricultural machinery needs to have a good path-tracking ability [4]. The value change of look-ahead distance, as the only parameter that can be adjusted in a pure pursuit model, has a great influence on the path-tracking ability of agricultural machinery [5]. A number of researchers have designed experiments and tests to improve the accuracy of path tracking. When the look-ahead distance is equal to the width of the wheel, the obtained path-tracking accuracy is limited [6]. With a tracking algorithm based on an optimal target point, although the accuracy of path tracking could be improved, it is too complex for environments with high real-time requirements such as greenhouses [7]. A pure pursuit model combined with GPS used to obtain the parameters and positions of a vehicle, as well as the look-ahead distance through simulation, could improve tracking accuracy but requires the simulation of different speeds and too-complicated operation [8]. In research on fixed look-ahead distance, researchers have found that a dynamic look-ahead distance performs better in path tracking. Although it considered the influence of speed, a pure pursuit model with a PID algorithm used to change the look-ahead distance was not found to be efficient [9]. Based on Kalman filtering and a pure tracking model, [PERSON] et al. [10] used the IATE optimization criterion to simulate the optimal look-ahead distance. Although the path-tracking ability was improved in their study, the application of various complex functions is needed, which is not conducive to the operation of agricultural machinery. A PI-based path controller that changes the look-ahead distance without considering the influence of speed is thus not dynamic [11]. An adaptive method that changes the look-ahead distance could improve the pure pursuit algorithm, but its algorithm requires extensive analysis that is too complex and not convenient for the operation of farm machinery [12]. Determining the look-ahead distance of a pure tracking model with the fuzzy adaptive method requires the construction of a self-adjustment function and complicated operation [13]. During travel, the determination of look-ahead distance is strongly related to each driver's experience, so researchers have attempted to find more intelligent methods to determine the optimal look-ahead distance. An adaptive, look-ahead distance, pure pursuit lateral controller that uses a greedy algorithm to improve the tracking accuracy was proposed, but it is not suitable for complex terrain situations [14]. A path-tracking model based on a fuzzy controller that could provide the optimal steering angle was constructed, but the vehicle structure and kinematics model were different from our test prototype [15]. Hu determined the optimal look-ahead distance according to the curvature of the reference path and the current speed [16]. An improved pure pursuit algorithm using AGV to predict the trajectory and turning speed could improve the accuracy of path pursuit, but the adopted technology is not suitable for agricultural conditions [17]. With an extended Kalman filter, the method could decompose speed into Cartesian components and improve path-tracking accuracy, but the amount of calculations was large and unsuitable for the operation of agricultural machinery [18].