An iterative learning control technique for mobile robot path-tracking control

Iterative learning control (ILC) is a technique for using repetitive operation to derive the input commands needed to force a dynamical system to follow a prescribed trajectory. In this paper we describe ideas towards the use of ILC for path-tracking control of a mobile robot. The work is focused on a novel robotic platform, the Utah State University (USU) Omni-Directional Vehicle (ODV), which features six "smart wheels," each of which has independent control of both speed and direction. Using a validated dynamic model of the ODV robot, it is shown that ILC can be used to learn the nominal input commands needed force the robot to track a prescribed path in inertial space.