Abstract: An integrated motion control model of path tracking and servo control is proposed for an automated guided vehicle (AGV) that suffers non-holonomic constraints and dynamic effects. Path tracking technique based on multi-step predictive optimal control and intelligent predictive iterative control is used to correct pose errors of AGV, which produces the limited speed difference output with the best coordination for error correction under velocity and acceleration constraints. Target velocities of two driving wheels are set according to this output and AGV moving speed. A multi-objective genetic algorithm takes servo control performances required by path tracking as its decision-making preferences, and it is used to optimize PID parameters of a servo controller that is responsible for eliminating velocity errors of driving wheels and making their actual velocities satisfy the requirements of path tracking. Velocity and acceleration constraints are adjusted as a feedback according to system response performances. AGV path tracking experiment demonstrates that this control model can match the capability of path tracking technique to correct pose errors with that of servo control technique to eliminate velocity errors, which can achieve the overall optimization of AGV motion performance.

Key words: Automated guided vehicle, Motion control, Path tracking, Servo control