Norm Optimal Iterative Learning Control for a Roll to Roll nano/micro-manufacturing system

Recent advances in micro/nano-scale manufacturing have transitioned from batch modes of fabrication on rigid substrates to continuous modes of fabrication on flexible substrates. The majority of these continuous systems utilize a Roll to Roll (R2R) system approach. To maximize the effectiveness of the R2R system it is important to maintain high precision motion and tension control. For micro/nano-manufacturing the continuous substrate is often processed using both stepping motions and continuous scanning motions. In this work, a Norm Optimal Iterative Learning Controller (NOILC) is utilized to simultaneously improve the position tracking precision, as well as the web tension regulation. The approach is demonstrated on an experimental testbed for both continuous and stepping trajectories with greatly improved performance compared to H₂ optimal feedback.