Robust iterative learning control for linear systems with multiple time-invariant parametric uncertainties

Hoa Dinh Nguyen, David Banjerdpongchai

研究成果: ジャーナルへの寄稿記事

抄録

This article presents a novel robust iterative learning control algorithm (ILC) for linear systems in the presence of multiple time-invariant parametric uncertainties.The robust design problem is formulated as a min-max problem with a quadratic performance criterion subject to constraints of the iterative control input update. Then, we propose a new methodology to find a sub-optimal solution of the min-max problem. By finding an upper bound of the worst-case performance, the min-max problem is relaxed to be a minimisation problem. Applying Lagrangian duality to this minimisation problem leads to a dual problem which can be reformulated as a convex optimisation problem over linear matrix inequalities (LMIs). An LMI-based ILC algorithm is given afterward and the convergence of the control input as well as the system error are proved. Finally, we apply the proposed ILC to a generic example and a distillation column. The numerical results reveal the effectiveness of the LMI-based algorithm.

元の言語英語
ページ(範囲)2506-2518
ページ数13
ジャーナルInternational Journal of Control
83
発行部数12
DOI
出版物ステータス出版済み - 12 1 2010

Fingerprint

Linear systems
Linear matrix inequalities
Convex optimization
Distillation columns
Uncertainty

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications

これを引用

Robust iterative learning control for linear systems with multiple time-invariant parametric uncertainties. / Nguyen, Hoa Dinh; Banjerdpongchai, David.

:: International Journal of Control, 巻 83, 番号 12, 01.12.2010, p. 2506-2518.

研究成果: ジャーナルへの寄稿記事

@article{52f433b168544f4ca9b1ce0ba2a28aff,
title = "Robust iterative learning control for linear systems with multiple time-invariant parametric uncertainties",
abstract = "This article presents a novel robust iterative learning control algorithm (ILC) for linear systems in the presence of multiple time-invariant parametric uncertainties.The robust design problem is formulated as a min-max problem with a quadratic performance criterion subject to constraints of the iterative control input update. Then, we propose a new methodology to find a sub-optimal solution of the min-max problem. By finding an upper bound of the worst-case performance, the min-max problem is relaxed to be a minimisation problem. Applying Lagrangian duality to this minimisation problem leads to a dual problem which can be reformulated as a convex optimisation problem over linear matrix inequalities (LMIs). An LMI-based ILC algorithm is given afterward and the convergence of the control input as well as the system error are proved. Finally, we apply the proposed ILC to a generic example and a distillation column. The numerical results reveal the effectiveness of the LMI-based algorithm.",
author = "Nguyen, {Hoa Dinh} and David Banjerdpongchai",
year = "2010",
month = "12",
day = "1",
doi = "10.1080/00207179.2010.531398",
language = "English",
volume = "83",
pages = "2506--2518",
journal = "International Journal of Control",
issn = "0020-7179",
publisher = "Taylor and Francis Ltd.",
number = "12",

}

TY - JOUR

T1 - Robust iterative learning control for linear systems with multiple time-invariant parametric uncertainties

AU - Nguyen, Hoa Dinh

AU - Banjerdpongchai, David

PY - 2010/12/1

Y1 - 2010/12/1

N2 - This article presents a novel robust iterative learning control algorithm (ILC) for linear systems in the presence of multiple time-invariant parametric uncertainties.The robust design problem is formulated as a min-max problem with a quadratic performance criterion subject to constraints of the iterative control input update. Then, we propose a new methodology to find a sub-optimal solution of the min-max problem. By finding an upper bound of the worst-case performance, the min-max problem is relaxed to be a minimisation problem. Applying Lagrangian duality to this minimisation problem leads to a dual problem which can be reformulated as a convex optimisation problem over linear matrix inequalities (LMIs). An LMI-based ILC algorithm is given afterward and the convergence of the control input as well as the system error are proved. Finally, we apply the proposed ILC to a generic example and a distillation column. The numerical results reveal the effectiveness of the LMI-based algorithm.

AB - This article presents a novel robust iterative learning control algorithm (ILC) for linear systems in the presence of multiple time-invariant parametric uncertainties.The robust design problem is formulated as a min-max problem with a quadratic performance criterion subject to constraints of the iterative control input update. Then, we propose a new methodology to find a sub-optimal solution of the min-max problem. By finding an upper bound of the worst-case performance, the min-max problem is relaxed to be a minimisation problem. Applying Lagrangian duality to this minimisation problem leads to a dual problem which can be reformulated as a convex optimisation problem over linear matrix inequalities (LMIs). An LMI-based ILC algorithm is given afterward and the convergence of the control input as well as the system error are proved. Finally, we apply the proposed ILC to a generic example and a distillation column. The numerical results reveal the effectiveness of the LMI-based algorithm.

UR - http://www.scopus.com/inward/record.url?scp=78650340270&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650340270&partnerID=8YFLogxK

U2 - 10.1080/00207179.2010.531398

DO - 10.1080/00207179.2010.531398

M3 - Article

AN - SCOPUS:78650340270

VL - 83

SP - 2506

EP - 2518

JO - International Journal of Control

JF - International Journal of Control

SN - 0020-7179

IS - 12

ER -