Research On Path Tracking Control Of Automatic Guidance Vehicle

The automatic guided vehicle is a kind of intelligent mobile robot which has the flexibility and complex needs material handling, has become one of the important equipment in large warehouse cargo handling and sorting. The accuracy of the path tracking control algorithm and the positioning algorithm of the automatic guidance vehicle determines whether AGV can complete the intended target task.Active disturbance rejection controller inherits the essence of classical PID technology and absorbs the results of modern control theory, by uing a new type of nonlinear feedback structure and the corresponding nonlinear control law,many nonlinear systems, which is difficult to control, was controlled dffectively.Bseide, it also made great achievement in application. But at the same time, the ADRC controller has many parameters, and it is difficult to tuning, so Professor Gao Zhiqiang transforms the nonlinear part of the ADRC to linear part, and proposes LADRC.It is difficult to set the parameters of the linear auto disturbance rejection controller with the traditional empirical method. Based on the framework of the standard quantum behaved particle swarm optimization algorithm, an improved chaotic quantum behaved particle swarm optimization algorithm is proposed..The basic idea is to change the contraction-expansion coefficient of quantum behaved particle swarm optimization by chaos optimization algorithm. When the quantum behaved particle swarm optimization algorithm is trapped into the local optimal solution, by changing the particle and introduces the chaos search guide particles escape from the local optimal solution to improve the accuracy of convergence.In order to solve the problem of path correction, analysis of the relationship between model orientation deviation and position deviation with driving wheel velocity. The driving system of the automatic guided vehicle is added into the kinematic model of the automatic guided vehicle to establish the spatial model of the automatic guided vehicle system.The nonlinear multi-input multi-output control system is decoupled into several independent single-input single-output linear control loops by decomposing the velocity of AGV into the components in the X and Y directions.The controller for trajectory tracking of AGV is designed based on the linear active disturbance rejection control (LADRC). According to the characteristics of the vehicle sensor, a method based on multi-sensor information fusion is used to estimate the position and orientation of the AGV in a given environment.In order to verify the correctness of the path tracking control algorithm and localization algorithm, the experiments are carried out on the simulation platform of AGV. The simulation results show that compared with the traditional PID controller, the linear auto-disturbance-rejection controller has a great advantage in the large deviation and the variation of the vehicle body's own parameters, which can quickly and effectively complete the path tracking.In this paper, the localization algorithm and the path tracking algorithm are used to debug the two-wheel differential drive AGV. The experimental results show that the proposed algorithm and the path tracking controller algorithm are feasible.