Convex optimization based iterative learning control for iteration-varying systems under output constraints

Xu Jin; Zhaowei Wang; Raymond H.S. Kwong

doi:10.1109/ICCA.2014.6871135

Convex optimization based iterative learning control for iteration-varying systems under output constraints

Xu Jin, Zhaowei Wang, Raymond H.S. Kwong

Mechanical and Aerospace Engineering

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

14 Scopus citations

Abstract

In this work, we discuss a class of linear iterative learning control (ILC) systems which are iteration-varying with system output constraints. It can be shown that the objective of ensuring convergence of system output tracking error and satisfying system output constraints can be converted to a convex optimization problem, in which the objective function is quadratic and the constraints are convex. Under the proposed algorithm, tracking error convergence can be guaranteed over the iteration domain. A simulation study based on a wafer stage system is presented to demonstrate the efficacy of our approach.

Original language	English
Title of host publication	11th IEEE International Conference on Control and Automation, IEEE ICCA 2014
Pages	1444-1448
Number of pages	5
DOIs	https://doi.org/10.1109/ICCA.2014.6871135
State	Published - 2014
Event	11th IEEE International Conference on Control and Automation, IEEE ICCA 2014 - Taichung, Taiwan, Province of China Duration: Jun 18 2014 → Jun 20 2014

Publication series

Name	IEEE International Conference on Control and Automation, ICCA
ISSN (Print)	1948-3449
ISSN (Electronic)	1948-3457

Conference

Conference	11th IEEE International Conference on Control and Automation, IEEE ICCA 2014
Country/Territory	Taiwan, Province of China
City	Taichung
Period	6/18/14 → 6/20/14

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering

Access to Document

10.1109/ICCA.2014.6871135

Cite this

Jin, X., Wang, Z., & Kwong, R. H. S. (2014). Convex optimization based iterative learning control for iteration-varying systems under output constraints. In 11th IEEE International Conference on Control and Automation, IEEE ICCA 2014 (pp. 1444-1448). Article 6871135 (IEEE International Conference on Control and Automation, ICCA). https://doi.org/10.1109/ICCA.2014.6871135

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title = "Convex optimization based iterative learning control for iteration-varying systems under output constraints",

abstract = "In this work, we discuss a class of linear iterative learning control (ILC) systems which are iteration-varying with system output constraints. It can be shown that the objective of ensuring convergence of system output tracking error and satisfying system output constraints can be converted to a convex optimization problem, in which the objective function is quadratic and the constraints are convex. Under the proposed algorithm, tracking error convergence can be guaranteed over the iteration domain. A simulation study based on a wafer stage system is presented to demonstrate the efficacy of our approach.",

author = "Xu Jin and Zhaowei Wang and Kwong, {Raymond H.S.}",

year = "2014",

doi = "10.1109/ICCA.2014.6871135",

language = "English",

isbn = "9781479928378",

series = "IEEE International Conference on Control and Automation, ICCA",

pages = "1444--1448",

booktitle = "11th IEEE International Conference on Control and Automation, IEEE ICCA 2014",

note = "11th IEEE International Conference on Control and Automation, IEEE ICCA 2014 ; Conference date: 18-06-2014 Through 20-06-2014",

}

Jin, X, Wang, Z & Kwong, RHS 2014, Convex optimization based iterative learning control for iteration-varying systems under output constraints. in 11th IEEE International Conference on Control and Automation, IEEE ICCA 2014., 6871135, IEEE International Conference on Control and Automation, ICCA, pp. 1444-1448, 11th IEEE International Conference on Control and Automation, IEEE ICCA 2014, Taichung, Taiwan, Province of China, 6/18/14. https://doi.org/10.1109/ICCA.2014.6871135

Convex optimization based iterative learning control for iteration-varying systems under output constraints. / Jin, Xu; Wang, Zhaowei; Kwong, Raymond H.S.
11th IEEE International Conference on Control and Automation, IEEE ICCA 2014. 2014. p. 1444-1448 6871135 (IEEE International Conference on Control and Automation, ICCA).

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

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N2 - In this work, we discuss a class of linear iterative learning control (ILC) systems which are iteration-varying with system output constraints. It can be shown that the objective of ensuring convergence of system output tracking error and satisfying system output constraints can be converted to a convex optimization problem, in which the objective function is quadratic and the constraints are convex. Under the proposed algorithm, tracking error convergence can be guaranteed over the iteration domain. A simulation study based on a wafer stage system is presented to demonstrate the efficacy of our approach.

AB - In this work, we discuss a class of linear iterative learning control (ILC) systems which are iteration-varying with system output constraints. It can be shown that the objective of ensuring convergence of system output tracking error and satisfying system output constraints can be converted to a convex optimization problem, in which the objective function is quadratic and the constraints are convex. Under the proposed algorithm, tracking error convergence can be guaranteed over the iteration domain. A simulation study based on a wafer stage system is presented to demonstrate the efficacy of our approach.

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BT - 11th IEEE International Conference on Control and Automation, IEEE ICCA 2014

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Y2 - 18 June 2014 through 20 June 2014

ER -

Convex optimization based iterative learning control for iteration-varying systems under output constraints

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