Time-varying norm optimal iterative learning identification

Nanjun Liu, Andrew Alleyne

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


In this paper, we focus on improving the performance of an Iterative Learning Identification (ILI) algorithm for identifying discrete, Single-Input Single-Output (SISO), Linear Time- Varying (LTV) plants that are able to repeat their trajectories. The identification learning laws are determined through an optimization framework, which is similar in nature to the design of norm optimal Iterative Learning Control (ILC). The ILI algorithm has been previously demonstrated to be capable of tracking rapid parameter changes. However, when it is applied to systems with noise, it results in high frequency parameter fluctuation around their true values. This paper suggests a time-varying ILI technique to improve the steady state estimation while maintaining the ILI's ability to track rapid parameter changes.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Number of pages6
StatePublished - 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Publication series

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


Other2013 1st American Control Conference, ACC 2013
Country/TerritoryUnited States
CityWashington, DC

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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