Sequential Quadratic Programming Iterative Learning Control for a Roll-to-Roll Manufacturing Process

Abstract Roll-to-roll (R2R) mechanical dry transfer is an enabling technology for high-throughput, environmentally friendly fabrication of advanced thin-film devices. However, precise control is required to ensure high-quality transfer, presenting a significant challenge due to nonlinear peeling dynamics, abrupt material property changes, and input constraints. This study proposes a sequential quadratic programming iterative learning control (SQP ILC) approach to regulate the R2R mechanical dry transfer process. The method leverages the system's iterative structure to improve the performance across successive transfer tasks while rigorously accounting for nonlinear dynamics and input constraints. Experimental validation on a lab-scale testbed and a case study on the chemical vapor deposition-grown graphene transfer show that the SQP ILC significantly improves transfer quality with minimal online computation, making it a scalable solution for industrial R2R applications.