Semi-active iterative learning control

Sandipan Mishra; Andrew G Alleyne

Semi-active iterative learning control

Sandipan Mishra, Andrew G Alleyne

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
Pages	3645-3650
Number of pages	6
State	Published - Sep 29 2011
Event	2011 American Control Conference, ACC 2011 - San Francisco, CA, United States Duration: Jun 29 2011 → Jul 1 2011

Publication series

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

Other

Other	2011 American Control Conference, ACC 2011
Country/Territory	United States
City	San Francisco, CA
Period	6/29/11 → 7/1/11

Keywords

Iterative Learning Control
Semi-Active Systems

ASJC Scopus subject areas

Electrical and Electronic Engineering

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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.",

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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.

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

T2 - 2011 American Control Conference, ACC 2011

Y2 - 29 June 2011 through 1 July 2011

ER -

Semi-active iterative learning control

Abstract

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Keywords

ASJC Scopus subject areas

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