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Data-Based Control for Learning Systems With Nonlinear Dynamics: A Case Study

  • Yuxin Wu*
  • , Deyuan Meng*
  • *Corresponding author for this work

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

Abstract

This paper focuses on exploiting a data-based control design framework for a class of iterative learning control (ILC) systems with nonlinear dynamics. A test method is first presented for nonlinear ILC systems to generate some helpful output data under some specific test inputs. Then, a data-based P-type ILC updating law is developed by resorting to these input and output data, for which a simple data-based selection condition on the gain matrix is provided to accomplish the perfect tracking objective of ILC systems having the globally Lipschitz nonlinear dynamics. The proposed data-based P-type ILC updating law is applicable for nonlinear ILC systems without the common full rank requirement, where none of the specific model information is utilized. A simulation example is given to illustrate the validity of the data-based ILC design framework for nonlinear ILC systems.

Original languageEnglish
Title of host publicationIFAC-PapersOnLine
EditorsHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
PublisherElsevier B.V.
Pages1913-1918
Number of pages6
Edition2
ISBN (Electronic)9781713872344
DOIs
StatePublished - 1 Jul 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023

Publication series

NameIFAC-PapersOnLine
Number2
Volume56
ISSN (Electronic)2405-8963

Conference

Conference22nd IFAC World Congress
Country/TerritoryJapan
CityYokohama
Period9/07/2314/07/23

Keywords

  • Data-based control design
  • global Lipschitz
  • iterative learning control
  • nonlinear system
  • perfect tracking

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