Higher-Order Spatial Iterative Learning Control for Additive Manufacturing

This paper presents a higher-order spatial iterative learning control (HO-SILC) framework for heightmap tracking of 3D structures that are fabricated by additive manufacturing (AM) technology. In the literature, first-order spatial ILC (FO-SILC) has been used in conjunction with additive processes to regulate single-layer structures. However, ILC has undeveloped potential to regulate AM structures that are fabricated by the repetitive addition of material in a layer-by-layer manner. Estimating the appropriate feedforward signal in these structures can be challenging due to iteration varying system parameters. In this paper, HO-SILC is used to iteratively construct the feedforward signal to improve device quality of 3D structures. To have a more realistic representation of the additive process, iteration varying uncertainties in the plant dynamics and non-repetitive noise in the input signal are included. We leverage the existing FO-SILC models in the literature and extend them to a HO-SILC framework that incorporates data available from a previously printed device, as well as multiple previously printed layers to enhance the overall performance. Subsequently, the monotonic and asymptotic stability conditions for the nominal HO-SILC algorithm are illustrated.