A convex optimization design of robust iterative learning control for linear systems with iteration-varying parametric uncertainties

Hoa Dinh Nguyen, David Banjerdpongchai

Research output: Contribution to journalArticlepeer-review

9 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 using 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 Lagrangian duality to this minimization problem results in a dual problem which can be reformulated as a convex optimization problem over linear matrix inequalities (LMIs). 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 distillation column to demonstrate its effectiveness.

Original languageEnglish
Pages (from-to)75-84
Number of pages10
JournalAsian Journal of Control
Volume13
Issue number1
DOIs
Publication statusPublished - Jan 1 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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