Comparison of Serial and Parallel Implementations of ILC in a Closed-Loop Feedback System

Abstract

Iterative Learning Control (ILC) is a data-driven strategy for precise trajectory tracking in systems that operate repetitively under similar reference trajectories, disturbances, and initial conditions. Despite more than three decades of development, some implementation aspects of the learned ILC signals remain underexplored. This paper compares serial and parallel ILC signal implementations within a closed-loop feedback system. Although both approaches have been equally addressed in the literature, our findings show that the serial implementation provides clear advantages in achieving desired performance.