An Improved Iterative Learning Control for Uncertain Multi-Axis Systems

Ashley A. Armstrong, Andrew G. Alleyne

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


For learning control algorithms to date, the convergence rate in the iteration domain depends on the level of plant knowledge. This work presents a Fast Cross-coupled Iterative Learning Control (F-CCILC) scheme to overcome the current limitations in learning control algorithms. F-CCILC achieves fast convergence for multi-input multi-output (MIMO) systems with high model uncertainty. The approach uses involves using 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 the approach does not require end-user tuning of arbitrary gains. In this paper, the design for the F-CCILC system is presented, and the performance of this system is compared to the performance of existing ILC control schemes via simulations and experimental testing. Compared to the current control methods, the simulation results demonstrate increased robustness and learning speeds for multi-axis systems with significant model uncertainty.

Original languageEnglish (US)
Title of host publication2020 American Control Conference, ACC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781538682661
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: Jul 1 2020Jul 3 2020

Publication series

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


Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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