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

Erick Sutanto, Andrew G Alleyne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Pages5935-5941
Number of pages7
StatePublished - Sep 11 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period6/17/136/19/13

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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    Sutanto, E., & Alleyne, A. G. (2013). Norm Optimal Iterative Learning Control for a Roll to Roll nano/micro-manufacturing system. In 2013 American Control Conference, ACC 2013 (pp. 5935-5941). [6580769] (Proceedings of the American Control Conference).