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

Erick Sutanto; Andrew G. Alleyne

doi:10.1109/acc.2013.6580769

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 proceeding › Conference contribution

9 Scopus citations

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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5935-5941
Number of pages	7
ISBN (Print)	9781479901777
DOIs	https://doi.org/10.1109/acc.2013.6580769
State	Published - 2013
Externally published	Yes
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/Territory	United States
City	Washington, DC
Period	6/17/13 → 6/19/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1109/acc.2013.6580769

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Norm Optimal Iterative Learning Control for a Roll to Roll nano/micro-manufacturing system. / Sutanto, Erick; Alleyne, Andrew G.
2013 American Control Conference, ACC 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 5935-5941 6580769 (Proceedings of the American Control Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sutanto, E & Alleyne, AG 2013, Norm Optimal Iterative Learning Control for a Roll to Roll nano/micro-manufacturing system. in 2013 American Control Conference, ACC 2013., 6580769, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers Inc., pp. 5935-5941, 2013 1st American Control Conference, ACC 2013, Washington, DC, United States, 6/17/13. https://doi.org/10.1109/acc.2013.6580769

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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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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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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

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

Abstract

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