Data-driven feedforward tuning using non-causal rational basis functions: With application to an industrial flatbed printer

Lennart Blanken (Corresponding author), Sjirk Koekebakker, Tom Oomen

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Data-driven feedforward tuning enables high performance for control systems that perform varying tasks by using past measurement data. The aim of this paper is to develop an approach for data-driven feedforward tuning that achieves high accuracy and at the same time is computationally inexpensive. A linear parametrization is employed that enables parsimonious modeling of inverse systems for feedforward through the use of non-causal rational orthonormal basis functions in L2. The benefits of the proposed parametrization are experimentally demonstrated on an industrial printer, including pre-actuation and cyclic pole repetition.

Original languageEnglish
Article number102424
Number of pages8
JournalMechatronics
Volume71
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Basis functions
  • Feedforward control
  • Learning control
  • Mechatronic systems
  • Motion control systems
  • Non-causal systems

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