Semi-active iterative learning control

Sandipan Mishra; Andrew Alleyne

doi:10.1109/acc.2011.5990632

Semi-active iterative learning control

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

Abstract

This paper presents an Iterative Learning Control (ILC) algorithm for iterative parameter update in a semi-active system. The ILC law is designed to minimize a cost function, for example, the mean squared tracking error. First, a parametrized lifted domain representation of a linear parameter-varying system is developed explicitly. Based on this lifted domain representation and a cost function, gradient-based laws for the parameter update from iteration to iteration are proposed. Stability, monotonicity, steady state error, and robustness properties of these algorithms are presented. Finally, an application of the proposed algorithm is illustrated through the simulation of a plastic blow molding system.

Original language	English (US)
Title of host publication	Proceedings of the 2011 American Control Conference, ACC 2011
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3645-3650
Number of pages	6
ISBN (Print)	9781457700804
DOIs	https://doi.org/10.1109/acc.2011.5990632
State	Published - 2011
Externally published	Yes

Publication series

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

Keywords

Iterative Learning Control
Semi-Active Systems

Publisher link

10.1109/acc.2011.5990632

Find It

Find It at U of M Libraries

Cite this

@inproceedings{90938bc83d9344159b7af5221ea4c515,

title = "Semi-active iterative learning control",

abstract = "This paper presents an Iterative Learning Control (ILC) algorithm for iterative parameter update in a semi-active system. The ILC law is designed to minimize a cost function, for example, the mean squared tracking error. First, a parametrized lifted domain representation of a linear parameter-varying system is developed explicitly. Based on this lifted domain representation and a cost function, gradient-based laws for the parameter update from iteration to iteration are proposed. Stability, monotonicity, steady state error, and robustness properties of these algorithms are presented. Finally, an application of the proposed algorithm is illustrated through the simulation of a plastic blow molding system.",

keywords = "Iterative Learning Control, Semi-Active Systems",

author = "Sandipan Mishra and Andrew Alleyne",

year = "2011",

doi = "10.1109/acc.2011.5990632",

language = "English (US)",

isbn = "9781457700804",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3645--3650",

booktitle = "Proceedings of the 2011 American Control Conference, ACC 2011",

}

TY - GEN

T1 - Semi-active iterative learning control

AU - Mishra, Sandipan

AU - Alleyne, Andrew

PY - 2011

Y1 - 2011

N2 - This paper presents an Iterative Learning Control (ILC) algorithm for iterative parameter update in a semi-active system. The ILC law is designed to minimize a cost function, for example, the mean squared tracking error. First, a parametrized lifted domain representation of a linear parameter-varying system is developed explicitly. Based on this lifted domain representation and a cost function, gradient-based laws for the parameter update from iteration to iteration are proposed. Stability, monotonicity, steady state error, and robustness properties of these algorithms are presented. Finally, an application of the proposed algorithm is illustrated through the simulation of a plastic blow molding system.

AB - This paper presents an Iterative Learning Control (ILC) algorithm for iterative parameter update in a semi-active system. The ILC law is designed to minimize a cost function, for example, the mean squared tracking error. First, a parametrized lifted domain representation of a linear parameter-varying system is developed explicitly. Based on this lifted domain representation and a cost function, gradient-based laws for the parameter update from iteration to iteration are proposed. Stability, monotonicity, steady state error, and robustness properties of these algorithms are presented. Finally, an application of the proposed algorithm is illustrated through the simulation of a plastic blow molding system.

KW - Iterative Learning Control

KW - Semi-Active Systems

UR - http://www.scopus.com/inward/record.url?scp=80053153830&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053153830&partnerID=8YFLogxK

U2 - 10.1109/acc.2011.5990632

DO - 10.1109/acc.2011.5990632

M3 - Conference contribution

AN - SCOPUS:80053153830

SN - 9781457700804

T3 - Proceedings of the American Control Conference

SP - 3645

EP - 3650

BT - Proceedings of the 2011 American Control Conference, ACC 2011

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Semi-active iterative learning control

Abstract

Publication series

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this