With the advancement of precision agriculture, the autonomous navigation of unmanned agricultural ground vehicles (UAGVs) has emerged as a critical research topic. As a fundamental component of autonomous navigation, path-tracking control is essential for ensuring the accurate and stable operation of UAGVs. However, achieving high-precision and robust tracking in agricultural environments remains challenging due to unstructured terrain, variable wheel slip, and complex dynamic disturbances. This review provides a structured and comprehensive survey of modeling and control methodologies for UAGVs, with particular emphasis on control-theoretic formulations and their applicability across diverse agricultural scenarios. In contrast to prior reviews, the modeling approaches are systematically classified into geometric, kinematic, and dynamic models, including extended formulations that incorporate wheel slip and external disturbances. Furthermore, this paper systematically reviews commonly adopted path-tracking strategies for UAGVs, including proportional–integral–derivative (PID) control, pure pursuit (PP), Stanley control, sliding mode control (SMC), model predictive control (MPC), and learning-based approaches. Emphasis is placed on their theoretical underpinnings, tracking accuracy, adaptability to unstructured field environments, and computational efficiency. In addition, several key technical challenges are identified, such as terrain-adaptive vehicle modeling, slip compensation mechanisms, real-time implementation under hardware constraints, and the cooperative control of multiple UAGVs operating in dynamic agricultural scenarios. By presenting a detailed review from a control-centric perspective, this study aims to serve as a valuable reference for researchers and practitioners developing intelligent agricultural vehicle systems.

This article proposes an optimal trajectory tracking method for a novel robotic platform featuring four-wheel independent driving and on-axle steering (4WID-OAS). Unlike conventional mobile robots, the 4WID-OAS robotic vehicle adopts a unique coordinated differential steering system that possesses the low torque characteristics of differential steering and the high maneuverability of four-wheel independently steering (4WIS). To enable autonomous trajectory tracking, a lightweight control scheme is developed by integrating a four-wheel speed distribution strategy with an optimal tracking control law. The maneuvering model of the 4WID-OAS robot is first investigated as the foundation for the trajectory tracking system. Subsequently, an explicit nonlinear model predictive control (ENMPC) strategy is designed to solve the optimal tracking problem. With the closed-form ENMPC solution, online optimization is not required, thereby enabling high control bandwidth of advanced control strategies with reduced cost. The stability of the system is rigorously proven. Comprehensive simulations and field experiments validate the superiority of the 4WID-OAS robot and demonstrate that the proposed controller effectively balances tracking accuracy with computational efficiency, facilitating real-time deployment on embedded platforms.