Multilayered Sampled-Data Iterative Learning Tracking for Discrete Systems with Cooperative-Antagonistic Interactions

Wenjun Xiong*, Daniel W. C. Ho, Long Xu

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

20 Citations (Scopus)

Abstract

The tracking for discrete systems is discussed by designing two kinds of multilayered iterative learning schemes with cooperative-antagonistic interactions in this paper. The definition of the signed graph is presented and iterative learning schemes are then designed to be multilayered and have cooperative-antagonistic interactions. Moreover, considering the limited bandwidth of information storage, the state information of these controllers is updated in light of previous learning iterations but not just dependent on the last iteration. Two simple criteria are addressed to discuss the tracking of discrete systems with multilayered and cooperative-antagonistic iterative schemes. The simulation results are shown to demonstrate the validity of the given criteria.
Original languageEnglish
Article number8725913
Pages (from-to)4420-4429
JournalIEEE Transactions on Cybernetics
Volume50
Issue number10
Online published29 May 2019
DOIs
Publication statusPublished - Oct 2020

Research Keywords

  • Cooperative-antagonistic interactions
  • iterative learning control (ILC)
  • multilayered systems
  • sampled-data learning control
  • signed graph

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