Robust iterative learning control for linear systems with iteration-varying parametric uncertainties

Dinh Hoa Nguyen, David Banjerdpongchai

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

5 Citations (Scopus)

Abstract

In this paper, a new robust Iterative Learning Control (ILC) algorithm has been proposed for linear systems In the presence of iteration-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem usmg a quadratic performance criterion subject to constraints of the control input update. An upper bound of the maximization problem is derived, then, the solution of the min-max problem is achieved by solving a minimization problem. Applying Lagrange duality to this minimization problem results in a dual problem which can be reformulated as a convex optimization problem over linear matrix inequalities (LMls). Next, we present an LMI-based algorithm for the robust ILC design and prove the convergence of the control input and the error. Finally, the proposed algorithm is applied to a flexible link to demonstrate its effectiveness.

Original languageEnglish
Title of host publicationProceedings of 2009 7th Asian Control Conference, ASCC 2009
Pages716-721
Number of pages6
Publication statusPublished - Dec 11 2009
Externally publishedYes
Event2009 7th Asian Control Conference, ASCC 2009 - Hong Kong, China
Duration: Aug 27 2009Aug 29 2009

Publication series

NameProceedings of 2009 7th Asian Control Conference, ASCC 2009

Other

Other2009 7th Asian Control Conference, ASCC 2009
CountryChina
CityHong Kong
Period8/27/098/29/09

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Robust iterative learning control for linear systems with iteration-varying parametric uncertainties'. Together they form a unique fingerprint.

Cite this