A new spatial Iterative Learning Control approach for improved micro-Additive Manufacturing

Iterative Learning Control (ILC) is an effective control algorithm for improving tracking performance in stable or stabilizable systems that track a repetitive trajectory in time. For systems designed to track a position reference in time, there is a natural map between the temporal and spatial domains and researchers have exploited this map to develop spatial adaptations of ILC. However, there are systems in which the spatial coordinate does not have a unique mapping between time and space, such as additive manufacturing systems utilizing raster trajectories. New methods must be developed for these systems. We present a novel reformulation of ILC that is completely based on spatial coordinates. Two-dimensional convolution, as compared to one-dimensional convolution employed in temporal ILC, is applied to innately inform the algorithm the spatial proximity of measured data points. We show that the algorithm can be rewritten as a standard lifted-domain ILC update law, however with an embedded spatial map. Simulations incorporating a model of material ejection in a micro-Additive Manufacturing system demonstrate spatial ILC efficacy.