Iterative learning spatial height control for layerwise processes

Layerwise processes are common in industrial applications and have been well-studied in the control literature. A layerwise process has repeated layers in a spatial and/or temporal domain, which may differ over iterations. Height map control for layerwise processes is an important control problem, especially in the context of model mismatch and process constraints. In this work, we provide a layerpreview iterative learning controller to develop a novel learning-based control framework for layerwise processes. We utilize both the measurement data from previous layers and the gradient information from the model of the layer-to-layer process to develop the learning controller. This structure provides a hybrid approach where data and model information is efficiently used for improved controller performance. Simulation case studies on an additive manufacturing process with layerwise varying dynamics illustrate the utility of our approach under constrained inputs. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.