Norm Optimal Cross-Coupled Iterative Learning Control

Kira Barton; Jeroen Van De Wijdeven; Andrew Alleyne; Okko Bosgra; Maarten Steinbuch

doi:10.1109/CDC.2008.4738973

Norm Optimal Cross-Coupled Iterative Learning Control

Kira Barton, Jeroen Van De Wijdeven, Andrew Alleyne, Okko Bosgra, Maarten Steinbuch

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

22 Scopus citations

Abstract

In this paper, we focus on improving contour tracking in precision motion control (PMC) applications through the use of Cross-Coupled Iterative Learning Control (CCILC). Initially, the relationship between individual axis errors and contour error is discussed, including insights into the different reasons for implementing CCILC versus individual axis ILC. A Norm Optimal (N.O.) framework is used to design optimal learning filters based on design objectives. The general N.O. Framework is reformatted to include the contour error, as well as individual axis errors. General guidelines for tuning the different weighting matrices are presented. The weighting approach of this framework enables one to focus on individual axis or contour tracking independently. The performance benefits of N.O. CCILC versus ILC are illustrated through simulation and experimental testing on a multi-axis robotic testbed.

Original language	English (US)
Title of host publication	Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3020-3025
Number of pages	6
ISBN (Print)	9781424431243
DOIs	https://doi.org/10.1109/CDC.2008.4738973
State	Published - 2008
Externally published	Yes
Event	47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico Duration: Dec 9 2008 → Dec 11 2008

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Other

Other	47th IEEE Conference on Decision and Control, CDC 2008
Country/Territory	Mexico
City	Cancun
Period	12/9/08 → 12/11/08

Publisher link

10.1109/CDC.2008.4738973

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Barton, K., Van De Wijdeven, J., Alleyne, A., Bosgra, O., & Steinbuch, M. (2008). Norm Optimal Cross-Coupled Iterative Learning Control. In Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008 (pp. 3020-3025). Article 4738973 (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2008.4738973

Norm Optimal Cross-Coupled Iterative Learning Control. / Barton, Kira; Van De Wijdeven, Jeroen; Alleyne, Andrew et al.
Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008. Institute of Electrical and Electronics Engineers Inc., 2008. p. 3020-3025 4738973 (Proceedings of the IEEE Conference on Decision and Control).

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

Barton, K, Van De Wijdeven, J, Alleyne, A, Bosgra, O & Steinbuch, M 2008, Norm Optimal Cross-Coupled Iterative Learning Control. in Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008., 4738973, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 3020-3025, 47th IEEE Conference on Decision and Control, CDC 2008, Cancun, Mexico, 12/9/08. https://doi.org/10.1109/CDC.2008.4738973

Barton K, Van De Wijdeven J, Alleyne A, Bosgra O, Steinbuch M. Norm Optimal Cross-Coupled Iterative Learning Control. In Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008. Institute of Electrical and Electronics Engineers Inc. 2008. p. 3020-3025. 4738973. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC.2008.4738973

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abstract = "In this paper, we focus on improving contour tracking in precision motion control (PMC) applications through the use of Cross-Coupled Iterative Learning Control (CCILC). Initially, the relationship between individual axis errors and contour error is discussed, including insights into the different reasons for implementing CCILC versus individual axis ILC. A Norm Optimal (N.O.) framework is used to design optimal learning filters based on design objectives. The general N.O. Framework is reformatted to include the contour error, as well as individual axis errors. General guidelines for tuning the different weighting matrices are presented. The weighting approach of this framework enables one to focus on individual axis or contour tracking independently. The performance benefits of N.O. CCILC versus ILC are illustrated through simulation and experimental testing on a multi-axis robotic testbed.",

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AB - In this paper, we focus on improving contour tracking in precision motion control (PMC) applications through the use of Cross-Coupled Iterative Learning Control (CCILC). Initially, the relationship between individual axis errors and contour error is discussed, including insights into the different reasons for implementing CCILC versus individual axis ILC. A Norm Optimal (N.O.) framework is used to design optimal learning filters based on design objectives. The general N.O. Framework is reformatted to include the contour error, as well as individual axis errors. General guidelines for tuning the different weighting matrices are presented. The weighting approach of this framework enables one to focus on individual axis or contour tracking independently. The performance benefits of N.O. CCILC versus ILC are illustrated through simulation and experimental testing on a multi-axis robotic testbed.

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Norm Optimal Cross-Coupled Iterative Learning Control

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