Trajectory tracking control of four-wheel steering automatic guided vehicle under the working condition of moving centroid

The centroid of the whole vehicle moves when the automatic guided vehicle (AGV) loads and unloads goods between stations in the intelligent factory, which reduces the trajectory tracking accuracy. To this end, the dynamics and kinematics models of a four-wheel steering AGV were established, and the Lyapunov direct method was used to construct a trajectory tracking controller with global asymptotic stability in this study. Based on the adaptive learning factor and inertia weight, an adaptive particle swarm optimization algorithm was designed to optimize the control parameters of the controller, and an adaptive global asymptotic tracking control (AGATC) controller was proposed. Under simulated working condition of moving centroid, the AGATC controller was compared with adaptive model predictive control (AMPC) controller, and the trajectory tracking simulation was carried out. The results show that the position deviation of the AGATC controller was 23.97% lower than that of the AMPC controller, and the trajectory tracking control accuracy is higher under the condition of moving centroid. Moreover, a prototype of AGV was developed, and the trajectory tracking control verification experiment was carried out. The results show that the error between simulation and experiment was less than 9.03%, which proves the authenticity and effectiveness of the AGATC controller. This study provides theoretical and experimental basis for intelligent factory to realize precision and intelligent handling technology.