Varying-order iterative learning control against perturbed initial conditions

A homing mechanism is required for repositioning as a system performs tasks repeatedly. By examining the effect of poor repositioning on the tracking performance of iterative learning control, this paper develops a varying-order learning approach for the performance improvement. Through varying-order learning, the resultant system output trajectory is ensured to follow a given trajectory with a lowered error bound, in comparison with the conventional fixed-order method. A discrete-time initial rectifying action is introduced in the formed varying-order learning algorithm, and a sufficient condition for convergence is derived. An implementable scheme is presented based on the proposed approach, and illustrated by numerical results of two examples of robotic manipulators.