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

Dinh Hoa Nguyen, David Banjerdpongchai

Research output: Contribution to journalArticle

23 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. The 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 performance, then formulates a non-convex quadratic minimization problem to get the update of iterative control inputs. Applying Lagrange duality, the Lagrange dual function of the non-convex 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 design. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)1054-1060
Number of pages7
JournalJournal of Process Control
Volume19
Issue number6
DOIs
Publication statusPublished - Jun 1 2009
Externally publishedYes

Fingerprint

Iterative Learning Control
Linear matrix inequalities
Matrix Inequality
Linear Inequalities
Lagrange
Min-max Problem
Worst-case Performance
Convex optimization
Convex Optimization
Convergence Properties
Minimization Problem
Linear systems
Duality
Update
Linear Systems
Upper bound
Uncertainty
Numerical Examples
Design

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Cite this

An LMI approach for robust iterative learning control with quadratic performance criterion. / Nguyen, Dinh Hoa; Banjerdpongchai, David.

In: Journal of Process Control, Vol. 19, No. 6, 01.06.2009, p. 1054-1060.

Research output: Contribution to journalArticle

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