Semi-active iterative learning control

Sandipan Mishra, Andrew G Alleyne

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


This paper presents an Iterative Learning Control (ILC) algorithm for iterative parameter update in a semi-active system. The ILC law is designed to minimize a cost function, for example, the mean squared tracking error. First, a parametrized lifted domain representation of a linear parameter-varying system is developed explicitly. Based on this lifted domain representation and a cost function, gradient-based laws for the parameter update from iteration to iteration are proposed. Stability, monotonicity, steady state error, and robustness properties of these algorithms are presented. Finally, an application of the proposed algorithm is illustrated through the simulation of a plastic blow molding system.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Number of pages6
StatePublished - Sep 29 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2011 American Control Conference, ACC 2011
Country/TerritoryUnited States
CitySan Francisco, CA


  • Iterative Learning Control
  • Semi-Active Systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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