Bumpless transfer for a flexible adaptation of Iterative Learning Control

This work builds upon a framework for improving trajectory flexibility in systems controlled by Iterative Learning Control (ILC). Here we focus on positioning systems, decomposing a class of trajectories into motion primitives, termed basis tasks. The correct input signal for each basis task is identified in a training routine with ILC. The main development of this paper is a framework to intelligently apply these basis task specific input signals using an adaptation of bumpless transfer techniques. Bumpless transfer is reoriented to seamlessly transition between open-loop ILC signals without attenuating signal content away from the transition points. Experimental results display the effectiveness of the proposed approach on a serial planar positioning robot. Two conditions on basis task sequencing are tested. One which satisfies constraints imposed by previous work, and a relaxed trajectory constraint case designed to further explore trajectory flexibility. Bumpless transfer recovers some of the performance lost by constraint relaxation.