An Improved Approach to Iterative Learning Control for Uncertain Systems

Ashley A. Armstrong, Amy J. Wagoner Johnson, Andrew G. Alleyne

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


For iterative learning control (ILC) algorithms to date, there is a fundamental tradeoff between plant model knowledge and convergence rate in the iteration domain. This article presents a new fast ILC (FILC) method that uses a novel error term in the ILC learning law based on techniques from sliding mode control (SMC). The input signal is guaranteed to remain bounded in the time and iteration domains and is insensitive to noise due to the unique structure of the FILC learning algorithm. Moreover, the FILC approach does not require end-user tuning of arbitrary gains, which is useful for uncertain systems with significant uncertainty. The stability and convergence properties for the FILC system are presented using the Lyapunov analysis techniques. Simulation and experimental system results on a manufacturing system compare FILC with the existing ILC techniques and demonstrate that FILC achieves improved iteration convergence while retaining stability when plant uncertainty is high.

Original languageEnglish (US)
Article number8922859
Pages (from-to)546-555
Number of pages10
JournalIEEE Transactions on Control Systems Technology
Issue number2
StatePublished - Mar 2021
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received October 16, 2019; accepted November 4, 2019. Date of publication December 4, 2019; date of current version February 9, 2021. Manuscript received in final form November 4, 2019. This work was supported in part by the National Science Foundation Graduate Research Fellowship Program (GRFP). Recommended by Associate Editor K. Barton. (Corresponding author: Ashley A. Armstrong.) The authors are with the Department of Mechanical Science and Engineering, University of Illinois at Urbana–Champaign, Urbana, IL 61801 USA (e-mail:;;

Publisher Copyright:
© 1993-2012 IEEE.


  • Iterative learning control (ILC)
  • manufacturing
  • motion control
  • uncertain systems


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