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)

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Samenvatting

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.
Originele taal-2Engels
Pagina's (van-tot)91-96
Aantal pagina's6
TijdschriftIFAC-PapersOnLine
Volume52
Nummer van het tijdschrift15
DOI's
StatusGepubliceerd - 4 sep 2019
Evenement8th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2019), and 11th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2019) Vienna, Austria - Vienna, Oostenrijk
Duur: 4 sep 20196 sep 2019
http://www.mechatronicsnolcos2019.org/

Trefwoorden

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

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