Kernel-based regression of non-causal systems for inverse model feedforward estimation

L.L.G. Blanken, J.W.A. van den Meijdenberg, T.A.E. Oomen

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

1 Citation (Scopus)
8 Downloads (Pure)

Abstract

Inversion-based feedforward control enables high performance for industrial motion systems. To this end, accurate knowledge of the inverse system is required, and non-causal control actions must be enabled. The aim of this paper is to accurately identify non-causal inverse models in view of high feedforward control performance. The developed method employs kernel-based regularization to minimize the mean squared error of the estimate. The performance benefits of the presented approach are demonstrated on an industrial printing system, including non-causal feedforward control actions.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages461-466
Number of pages6
ISBN (Electronic)9781538619469
DOIs
Publication statusPublished - 1 Jun 2018
Event15th International Workshop on Advanced Motion Control (AMC 2018) - Shibaura Institute of Technology, Tokyo, Japan
Duration: 9 Mar 201811 Mar 2018
Conference number: 15
http://ewh.ieee.org/conf/amc/2018/

Conference

Conference15th International Workshop on Advanced Motion Control (AMC 2018)
Abbreviated titleAMC 2018
CountryJapan
CityTokyo
Period9/03/1811/03/18
OtherAMC2018 is the 15th in a series of biennial workshops that brings together researchers active in the field of advanced motion control to discuss current developments and future perspectives on motion control technology and applications. The workshop will be held at Shibaura Institute of Technology, Tokyo, Japan, during March 9-11, 2018.
Internet address

Keywords

  • Feedforward control
  • Gaussian process regression
  • Motion control
  • Regularization
  • System identification

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