Learning control without prior models: multi-variable model-free IIC: with application to a wide-format printer

Robin de Rozario (Corresponding author), Tom A.E. Oomen (Corresponding author)

Research output: Contribution to journalConference articlepeer-review

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Abstract

Learning control enables performance improvement of mechatronic systems that operate in a repetitive manner. Achieving desirable learning behavior typically requires prior knowledge in the form of a model. The prior modeling requirements can be significantly reduced by using past operational data to estimate this model during the learning process. The aim of this paper is to develop such a data-driven learning control method for multi-variable systems, which requires that directionality aspects are properly addressed. This is achieved by using multiple past experiments to estimate a frequency response function of the inverse dynamics while ensuring smooth convergence by using smoothed pseudo inversion. The developed method is successfully applied to an industrial wide-format printer, resulting in high performance.
Original languageEnglish
Pages (from-to)91-96
Number of pages6
JournalIFAC-PapersOnLine
Volume52
Issue number15
DOIs
Publication statusPublished - 4 Sep 2019
Event8th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2019), and 11th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2019) Vienna, Austria - Vienna, Austria
Duration: 4 Sep 20196 Sep 2019
http://www.mechatronicsnolcos2019.org/

Keywords

  • Convergence analysis
  • Frequency response methods
  • Linear multivariable systems
  • Nonlinear analysis
  • System identification

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