Sparse iterative learning control with application to a wafer stage: achieving performance, resource efficiency, and task flexibility

T.A.E. Oomen, C.R. Rojas

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
4 Downloads (Pure)

Abstract

Trial-varying disturbances are a key concern in Iterative Learning Control (ILC) and may lead to inefficient and expensive implementations and severe performance deterioration. The aim of this paper is to develop a general framework for optimization-based ILC that allows for enforcing additional structure, including sparsity. The proposed method enforces sparsity in a generalized setting through convex relaxations using ℓ1 norms. The proposed ILC framework is applied to the optimization of sampling sequences for resource efficient implementation, trial-varying disturbance attenuation, and basis function selection. The framework has a large potential in control applications such as mechatronics, as is confirmed through an application on a wafer stage.

Original languageEnglish
Pages (from-to)134-147
Number of pages14
JournalMechatronics
Volume47
DOIs
Publication statusPublished - 1 Nov 2017

Keywords

  • Feedforward
  • Iterative learning control
  • Motion control
  • Resource-efficient control
  • Sparse optimization

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