Trackability-Based Tracking Control for Stochastic Learning Systems: A Two-Dimensional System Method

  • Wenjin Lv
  • , Jingyao Zhang
  • , Deyuan Meng*
  • *Corresponding author for this work

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

Abstract

This paper focuses on exploring a novel trackability-based framework for the iterative learning control (ILC) systems subject to stochastic disturbances by using a two-dimensional (2-D) system method. By examining the fundamental trackability property of the stochastic ILC systems, the trackability-based stochastic ILC design and analysis framework is developed, eliminating the need for the common realizability assumption. Under this framework, thanks to the 2-D system method with the Roesser systems, the convergence results for both the output and input errors can be established under a unified condition, regardless of the full column or row rank of the input-output coupling matrix. A simulation example is included to demonstrate the validity of our proposed stochastic ILC design framework for ILC systems.

Original languageEnglish
Title of host publication2024 IEEE 63rd Conference on Decision and Control, CDC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4151-4156
Number of pages6
ISBN (Electronic)9798350316339
DOIs
StatePublished - 2024
Event63rd IEEE Conference on Decision and Control, CDC 2024 - Milan, Italy
Duration: 16 Dec 202419 Dec 2024

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference63rd IEEE Conference on Decision and Control, CDC 2024
Country/TerritoryItaly
CityMilan
Period16/12/2419/12/24

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