A review of path tracking control methods for wheeled mobile robots

With the rapid advancement of technology, wheeled mobile robots have been increasingly widely applied in industrial production, logistics transportation, service sectors, and even daily life. Trajectory tracking accuracy is critical for achieving autonomous navigation and efficient operations. Traditional PID (Proportional-Integral-Differential control) controllers often exhibit limited tunability and inadequate disturbance rejection capabilities when dealing with system nonlinearities and external disturbances. This paper systematically reviews the research progress in path tracking control for wheeled mobile robots over the past five years, drawing on existing literature and empirical data. First, based on kinematic models, existing methods are categorized into four major categories: classical PID-based, intelligent PID-based, Model Predictive Control (MPC)-based, and data-driven. Their research advancements in tracking accuracy and disturbance rejection capabilities are analyzed. Through the classification and synthesis of relevant research, the paper identifies bottlenecks including complex ground disturbances and dynamic target tracking. It highlights that improvements in PID control primarily proceed along three directions: adaptive parameter tuning to enable automatic adjustment; intelligent algorithm fusion to enhance processing capabilities while maintaining simplicity; and enhanced disturbance rejection capabilities to guarantee reliability in practical applications. This review provides a roadmap for subsequent research and engineering selection.