# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 1 Introduction
Most of the above research is to improve tracking accuracy for the purpose of control algorithm design. The transportation of heavy parts has high labor intensity, high safety risk, and low workpiece transfer efficiency. It is urgent to improve the level of intelligence and unmanned, which is of great significance for the research of heavy-duty AGVs [25]. Compared with light-load AGV, heavy-load AGV has a larger load and higher center of gravity. More stability problems need to be considered in the design of control methods, and the control system is more complex. In order to improve the handling stability of electric vehicles, [PERSON] et al. [26] designed a three-layer control strategy to improve the driving stability of the vehicle body by controlling the yaw rate and centroid sideslip angle of the vehicle body. The effectiveness of the proposed control strategy is verified by simulation and experiment. [PERSON] et al. [27] used the centroid sideslip angle as an index to evaluate the rollover behavior of AGV and used the multi-body dynamics simulation model to analyze the influence of turning rate, center of mass height, and pavement adhesion coefficient on the rollover stability of heavy-duty AGV. Aiming at the stability and safety problems caused by the failure of the actuator, [PERSON] et al. [28] designed a top-level optimal input controller based on MPC theory, a middle-level expected yaw moment solver based on fuzzy theory, and a bottom-level torque reconfiguration control distributor based on control gain. The hierarchical controller, and through simulation and experiment, proves that the yaw rate and the centroid sideslip angle of the center of mass can track the theoretical value well, and the designed controller is effective. Aiming at the problem that the centroid of AGV changes due to the irregular position and uneven weight of goods on the cargo on the load platform, [PERSON] et al. [29] proposed a hierarchical control strategy to improve yaw stability considering centroid variation, which reduced the yaw rate and centroid sideslip angle of AGV. Most of the above studies have improved the stability of heavy-duty AGVs by reducing the yaw rate and centroid sideslip angle of the center of mass during heavy-duty AGV driving. However, due to the large difference in the driving force required by the left and right wheels during heavy-duty AGV driving caused by load transfer and the unreasonable distribution of the driving force required by the front and rear wheels, the problem of large adhesion rate has not been considered. The above research optimizes different control methods for various application scenarios of heavy-duty AGVs to meet their special operational requirements. The heavy-duty AGV studied in this paper has the characteristic of large inertia. During driving in outdoor industrial areas, significant lateral and longitudinal load transfers are prone to occur during starting, braking, turning, or encountering potholes, speed bumps, and other conditions. For the vehicle body, lateral load transfer causes uneven wheel loads on both sides, leading to changes in the friction between the AGV wheels and the road surface. The power of the drive motor in this paper is almost equally distributed to the two side wheels through the differential, which can cause excessive torque on the inner wheels and insufficient torque on the outer wheels. Increasing the motor torque output may lead to inner wheel slippage or drive motor overload. Longitudinal load transfer changes the vertical loads on the front and rear wheels. When one wheel has a low load and high driving torque, wheel slippage may occur due to a high wheel adhesion rate. Therefore, both lateral and longitudinal load transfers have adverse effects on vehicle driving stability. To reduce these adverse effects while considering tracking accuracy, this paper designs a control strategy for heavy-duty low-speed AGVs with double Ackermann steering.