Implementation of the Iterative Learning Control in feedback controlled systems

Iterative Learning Control (ILC) is a data-driven control strategy designed for systems that continuously repeat their reference trajectories and operating conditions. For example, robotic systems in manufacturing often exhibit such behavior. The basic idea is that the system, by learning from the errors made in previous iterations, generates an ILC signal - a compensatory control component that adjusts the overall control input to improve tracking accuracy. This paper analyzes different approaches to the synthesis and implementation of the ILC signal in closed-loop feedback systems, with a particular focus on serial and parallel implementations of the ILC signal. The results demonstrate that the serial implementation can offer significant advantages in achieving the desired system performance.