During the operation of outdoor heavy-duty Automated Guided Vehicle (AGV), the stability and safety of AGV are easily reduced due to load transfer. In order to solve this problem, a trajectory tracking control strategy considering load transfer is proposed to realize the trajectory tracking of AGV and the adaptive distribution of driving torque. The three-degree-of-freedom (3-DOF) kinematics model and pose error model of heavy-duty AGV vehicles are established. The lateral load transfer and longitudinal load transfer rules are analyzed. The vehicle trajectory tracking control strategy is composed of an improved model predictive controller (IMPC) and drive motor torque adaptive distribution controller considering load transfer. By optimizing the lateral acceleration of the vehicle body, the IMPC controller improves the problem of large driving force difference between the left and right sides of the wheel caused by the lateral transfer of the load and the problem of large wheel adhesion rate caused by the longitudinal transfer of the load is improved by the speed controller and the torque proportional distribution controller. The joint simulation platform of MATLAB/Simulink and CarSim is built to simulate and analyze the trajectory tracking of heavy-duty AGV under different pavement adhesion coefficients. The simulation results have shown that compared with the control strategy without considering load transfer, on the two types of pavements with different adhesion coefficients, the maximum lateral acceleration is reduced by 19.7%, and the maximum tire adhesion rate is reduced by 11.5%.

A four-steering-wheel heavy-duty Automated Guided Vehicle (AGV) is prone to lateral instability and wheel slippage during acceleration, climbing, and small-radius turns. To address this issue, a trajectory tracking strategy considering lateral stability and an optimal driving torque distribution strategy considering load transfer and tire adhesion coefficient are proposed. Firstly, a three-degree-of-freedom AGV trajectory tracking model is established, tracking error and sideslip angle are incorporated into the cost function, and an improved model predictive trajectory tracking controller is proposed. Secondly, the longitudinal and yaw dynamic model of AGV is established, and vertical load transfer is analyzed. With the goal of minimizing tire adhesion utilization rate, quadratic programming is used for the optimal distribution of driving torque. Finally, through co-simulation using ADAMS and MATLAB on a narrow “climbing straight+ S-curve” road, the maximum tracking error is 0.0443 m. Compared to the unimproved model predictive control and average driving torque distribution strategy, the sideslip angle is reduced by 58.18%, the maximum tire adhesion utilization rate is reduced by 6.62%, and climbing gradeability on wet roads is enhanced.