## 1 Introduction  
With the advancement of industrialisation and urbanisation, a large number of labourers are gradually transferred from the countryside to the city, and the shortage of rural labourers makes the search for diversified means of agricultural production mechanisation gradually become an important element in promoting agricultural development. Agricultural mechanism is the key to agricultural modernisation and sustainable development, which not only has a profound impact on agricultural production but also promotes the development and upgrading of related industries [1]. The traditional agricultural equipment has a low degree of automation, needing professional drivers to operate, with high labour costs, and the quality of operation is easily affected by the level and fatigue of drivers, which makes it difficult to ensure the operational efficiency and intelligence level of agricultural production [2]. Therefore, the design of an automatic driving and operation system of agricultural machinery is the key to alleviate the shortage of rural labour. Agricultural mechanisation, as an important means to promote the improvement in agricultural production efficiency, is gradually moving towards intelligence and automation, especially the research and application of unmanned agricultural machinery, which has become a hotspot and trend in the field of agricultural mechanisation. At present, the automatic driving technology of agricultural machinery mainly focuses on path planning, obstacle avoidance, operation control, etc. Although significant progress has been made in recent years, there are still problems, such as low precision and poor adaptability to complex environments [3; 4]. [PERSON] et al. [5] established a kinematic model for agricultural machinery based on posture correction to address the issues of sideslip and slippage in muddy, hard-bottomed, and uneven paddy fields. This model is based on an attitude prediction control algorithm for linear model design and path tracking control research of paddy field agricultural machinery. The results show that the average root mean square error of this method in three-line straight path tracking is 0.043 m, which can effectively suppress lateral position mutations and improve control accuracy. [PERSON] et al. [6] proposed a new adaptive second-order sliding mode control method to solve the path tracking control problem during unmanned tractor operation. By using an improved increased power integrator and adaptive mechanism, an AOSSM controller was designed. The results showed that this method can effectively improve the chattering problem in traditional sliding mode control, and the control method has good applicability in vehicle models. [PERSON] et al. [7] proposed a novel adaptive sliding mode control for path tracking of unmanned agricultural vehicles, which uses a sliding mode observer to achieve system state estimation and adaptively adjust the uncertainty limit of lateral stiffness. This method exhibits good robustness, and its simulation results also indicate good performance. Previous methods have shown that traditional control algorithms often struggle to cope with changing operating conditions and environmental disturbances, which can affect the efficiency of agricultural machinery applications, especially in complex agricultural environments. However, in the past, there has been relatively little discussion on the agricultural machinery autonomous driving problem in the agricultural working environment, and the control algorithms involved mostly rely on model parameters. The actual agricultural machinery operation is more complex, and overly complex algorithm models may reduce operational efficiency. Therefore, research is conducted on the overall system architecture design based on traditional agricultural machinery, and in the autonomous driving part, fuzzy control algorithms and kinematic models are introduced to optimise control parameters through adaptive mechanisms in order to improve adaptability to complex environments and control accuracy.