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

Dinh Hoa Nguyen, David Banjerdpongchai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
Pages1805-1810
Number of pages6
DOIs
Publication statusPublished - Dec 1 2008
Externally publishedYes
Event2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 - Hanoi, Viet Nam
Duration: Dec 17 2008Dec 20 2008

Publication series

Name2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008

Other

Other2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
CountryViet Nam
CityHanoi
Period12/17/0812/20/08

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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