Enhancement of Iterative Learning Control With a Novel Error-Adaptive Weighting Mechanism

This paper introduces an error-adaptive weighting mechanism (EWM) for iterative learning control (ILC), enhancing convergence by incorporating an iteration-dependent weighting matrix into the learning gain. The EWM's adaptive weights, responsive to error magnitudes, particularly boost the reduction of large errors. The method's ability to accelerate convergence while ensuring perfect tracking is rigorously proven. The EWM, being data-driven and easily integrable with existing ILC schemes, is validated through numerical simulations comparing typical ILC schemes and their EWM-enhanced versions.