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A modified Q-filter model-inverse based ILC and its application on PMLSM

  • Jun Cao
  • , Yang Liu*
  • , Li Li
  • , Xiuyan Peng
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Harbin Engineering University

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

Abstract

Iterative learning control (ILC) is essential for the achievement of high servo performance for linear motors. This paper investigates a modified Q-filter model-inversion based ILC. Compared to existing model-inversion based ILC algorithms, two distinct features make the modified algorithm appealing: 1) The tradeoff that must be made by the traditional Q-filter model-inversion based ILC between robustness and converged error is removed. 2) The robustness to uncertainties is enhanced without the deterioration of asymptotic. The effectiveness and superiority of the proposed Q-filter are illustrated by both theoretical analysis and experimental results.

Original languageEnglish
Title of host publicationProceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1112-1117
Number of pages6
ISBN (Electronic)9781538626184
DOIs
StatePublished - 30 Oct 2018
Event7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 - Enshi, Hubei Province, China
Duration: 25 May 201827 May 2018

Publication series

NameProceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

Conference

Conference7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018
Country/TerritoryChina
CityEnshi, Hubei Province
Period25/05/1827/05/18

Keywords

  • Iterative learning control
  • Linear motor
  • Model-inverse iterative learning control
  • Wafer stage

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