An LMI approach for robust iterative learning control with quadratic performance criterion

Dinh Hoa Nguyen, David Banjerdpongchai

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

This paper presents the design of Iterative Learning Control based on Quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. Robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case error, then formulates a nonconvex quadratic minimization problem to get the update of iterative control inputs. Applying Langrange duality, the Lagrange dual function of the nonconvex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.

本文言語英語
ホスト出版物のタイトル2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
ページ1805-1810
ページ数6
DOI
出版ステータス出版済み - 2008
外部発表はい
イベント2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 - Hanoi, ベトナム
継続期間: 12 17 200812 20 2008

出版物シリーズ

名前2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008

その他

その他2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
国/地域ベトナム
CityHanoi
Period12/17/0812/20/08

All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識
  • 制御およびシステム工学
  • 電子工学および電気工学

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