AGV controller based on improved particle swarm optimization

As a high-end equipment for automated logistics, AGV has been widely used in workshops, warehouses and other places. AGV achieves the purpose of navigation through the identification and tracking of the route signs, which requires improving the tracking accuracy. A particle swarm algorithm is used to optimize the AGV controller parameters so that the AGV tracking bias is as small as possible. Firstly, the kinematics model of AGV is analyzed, and the PID controller optimized by particle swarm optimization algorithm is established. Then the car control is simulated in Matlab. By comparing the performance indexes of the system before and after optimization, the optimized PID controller can be seen. The performance of the car controller is significantly improved.