# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 1 Introduction  
With the proposal of the Made in China 2025 plan, the intelligence level of China's manufacturing industry is constantly improving, and the degree of unmanned is getting higher and higher. Automatic Guided Vehicle (AGV) is generally composed of a mechanical structure, power unit, and control system. It can travel autonomously according to the route specified by the dispatching system and avoid obstacles automatically [1]. At present, AGV is widely used in logistics warehousing [2], manufacturing [3], port terminals [4], aircraft assembly [5], and other fields, which greatly improves production efficiency and industrial competitiveness. The application and research of AGV mostly focus on structural design and optimization [6], path planning [7], trajectory tracking [8], and scheduling [9] of light-load and small-size AGV. Using reasonable control methods to achieve trajectory tracking is one of the important tasks of AGV operation. The commonly used trajectory tracking control methods are classical proportional-integral-derivative (PID) control [10], MPC [11], slidingmode control (SMC) [12], _H_e_o robust control [13], fuzzy intelligent control method [14], and the combination of two or more different control methods, such as fuzzy PID control [15], adaptive sliding mode control [16], etc. [PERSON] et al. [17] proposed a new backstepping and fuzzy sliding mode controller for differential wheeled mobile robots. Based on the kinematic model, the backstepping kinematic controller is established. Based on the dynamic model, the integer order integral sliding mode controller is established. At the same time, the fuzzy controller is used to estimate and compensate for the disturbance, which has a good control effect. [PERSON] et al. [18] proposed an adaptive learning model predictive control scheme. Based on the AGV kinematics model, the estimated system parameters are used in the MPC to improve the prediction accuracy. [PERSON] et al. [19] designed a robust, non-chattering sliding mode control method for linear following robots and carried out numerical solutions and experimental research. Compared with the traditional PID control method, the sliding mode controller has higher path-tracking efficiency. [PERSON] et al. [20] proposed a new dynamic coefficient reaching law to speed up the response speed of the sliding mode control system and designed a double closed-loop control to realize the trajectory tracking of the automatic guided vehicle. [PERSON] et al. [21] proposed a robust model predictive control strategy to realize the trajectory tracking of omnidirectional AGV under different constraints and applied the delayed neural network (DNN) to solve the quadratic programming (QP) problem. Compared with the traditional neural network, the computational complexity is reduced, and the online solution speed is improved. The robustness and effectiveness of the proposed method are verified by simulation analysis. [PERSON] et al. [22] proposed a robust nonsingular terminal sliding mode (NTSM) control method based on a recurrent neural network (RNN) structure to improve the trajectory tracking performance of AGV. The robustness and effectiveness of the proposed control method for parameter uncertainties and unknown external disturbances are verified by simulation. [PERSON] et al. [23] designed an adaptive sliding mode dynamic controller based on disturbance observer, which realized the online estimation of disturbance and completely eliminated the chattering phenomenon. The effectiveness of the proposed scheme is verified by simulation. [PERSON] et al. [24] proposed an adaptive neural network control scheme for uncertain wheeled mobile robots with speed constraints and nonholonomic constraints. For the uncertainty of the robot, the adaptive neural network is used to approximate the unknown robot dynamics and the barrier Lyapunov function is used to guarantee the constraint on velocity. The effectiveness of the proposed control scheme is proved by simulation research and practical experiments.