Iterative Learning Control for Nonlinear Systems with Iteration-Varying Trial Lengths Using Modified Reference Trajectories

Xingzheng Wu, Xianming Wang, Wen Qin, Liwei Li, Zhenxing Sun, Mouquan Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this work, a nonlinear uncertain system's iterative learning control (ILC) with randomly varying iteration lengths is discussed. Based on existing adaptive iterative learning control schemes, by using a modified reference trajectory, instead of assuming the identical initial condition, the alignment condition which is widely used in ILC problems can be satisfied when the iteration lengths is not fixed. An illustrative example is presented in the end to demonstrate the convergence of tracking error.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021
EditorsMingxuan Sun, Huaguang Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1161-1165
Number of pages5
ISBN (Electronic)9781665424233
DOIs
StatePublished - 14 May 2021
Event10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 - Suzhou, China
Duration: 14 May 202116 May 2021

Publication series

NameProceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021

Conference

Conference10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021
Country/TerritoryChina
CitySuzhou
Period14/05/2116/05/21

Keywords

  • alignment condition
  • iteration-varying lengths
  • iterative learning control(ILC)
  • nonlinear uncertain systems

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