Iterative learning control with parameter estimation for non-repetitive time-varying systems

Lei Wang, Ziwei Huangfu, Ruiwen Li, Xiewen Wen, Yuan Sun (Corresponding Author), Yiyang Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


This paper presents an extension method of iterative learning control (ILC) to address the applications associated with non-repetitive time-varying systems (NTVSs). Conventional ILC approaches employ fixed nominal system models, but non-repetitive time-varying models may lead to accumulated model uncertainties, which fails to satisfy the robust convergence conditions. To tackle this issue, a novel ILC algorithm with parameter estimation is proposed using back propagation neural network. This algorithm incorporates an approach that utilizes Bayesian regularization training mechanism to accurately estimate non-repetitive time-varying parameters. Through comprehensive experiment on Monolithic XY Stage, the performance of proposed algorithm is validated to demonstrate its feasibility and effectiveness while handling tasks on NTVSs.

Original languageEnglish
Pages (from-to)1455-1466
Number of pages12
JournalJournal of the Franklin Institute
Issue number3
Publication statusPublished - Feb 2024


  • Bayesian regularization
  • Iterative learning control
  • Neural network
  • Non-repetitive time-varying systems
  • Parameter estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics


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