To address the challenges of high-frequency oscillations and difficulty in parameter tuning typically encountered in traditional sliding mode control (SC) for path tracking in agricultural machinery, a novel control approach was proposed. The research focused on a tracked agricultural chassis, where a kinematic model and deviation dynamic equations were firstly developed. A super-twisting sliding mode control (TSC) law was then designed to ensure accurate path tracking. In addition, a parameter pre- tuning controller was introduced to facilitate the fine-tuning of control parameters. The finite memory Broyden algorithm was employed to optimize the parameters of the super-twisting sliding mode controller, enhancing its robustness. The stability of the path tracking system was verified by using Lyapunov stability analysis, ensuring that the proposed control method maintained stability under various operational conditions. Simulation results demonstrated that when the tracked agricultural chassis operated at a speed of 1.0 m / s, the maximum absolute tracking error was reduced to 0.063 m, and the average absolute error was minimized to 0.013 m. When compared with traditional linear PID control and conventional sliding mode control, the maximum deviation was reduced by 61.3% and 62.1% , respectively. Furthermore, the absolute average error was decreased by 89.2% and 75.4% , respectively. These results indicated a significant improvement in tracking accuracy with the proposed method. Field test results further validated the effectiveness of the control algorithm. When the operational speeds were 0.5 m / s and 1.0 m / s, the absolute average error of the parameter pre-tuned super-twisting sliding mode control algorithm was reduced by 69.2% and 50% , respectively, compared with that of the traditional sliding mode control. Additionally, the heading error was reduced by 61.1% and 40% , respectively, contributing to a noticeable reduction in high-frequency oscillations during operation. Overall, the proposed TSC strategy significantly enhanced the path tracking performance of agricultural machinery, providing a more stable and reliable control method compared with existing approaches. The method not only improved tracking accuracy but also effectively mitigated high-frequency oscillations, making it suitable for real-world applications in agricultural vehicle guidance systems.