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

Hoa Dinh Nguyen, David Banjerdpongchai

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

6 Citations (Scopus)

Abstract

In this paper, we present a robust Iterative Learning Control (ILC) design for linear systems in the presence of time-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 where the system model contains time-varying parametric uncertainties. An upper bound of the worst-case performance is employed in the min-max problem. Subsequently, applying Lagrangian duality to the min-max problem, we derive a dual problem which is reformulated as a convex optimization over linear matrix inequalities (LMIs). As a result, iterative input updates can be obtained by solving a series of LMI problems. We give an LMI algorithm for the robust ILC design and prove the convergence of the control input and the error. Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
Pages428-433
Number of pages6
DOIs
Publication statusPublished - Dec 1 2009
Externally publishedYes
Event48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 - Shanghai, China
Duration: Dec 15 2009Dec 18 2009

Other

Other48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
Country/TerritoryChina
CityShanghai
Period12/15/0912/18/09

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

Fingerprint

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

Cite this