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

Erick Sutanto; Andrew G Alleyne

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2013 American Control Conference, ACC 2013
Pages	5935-5941
Number of pages	7
State	Published - Sep 11 2013
Event	2013 1st American Control Conference, ACC 2013 - Washington, DC, United States Duration: Jun 17 2013 → Jun 19 2013

Publication series

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

Other

Other	2013 1st American Control Conference, ACC 2013
Country	United States
City	Washington, DC
Period	6/17/13 → 6/19/13

ASJC Scopus subject areas

Electrical and Electronic Engineering

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title = "Norm Optimal Iterative Learning Control for a Roll to Roll nano/micro-manufacturing system",

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.",

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N2 - 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.

AB - 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.

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