Learning Control without Prior Models: Multi-Variable Model-Free IIC, with Application to a Wide-Format Printer

Robin De Rozario1, Tom Oomen1

  • 1Eindhoven University of Technology

Details

14:50 - 15:10 | Wed 4 Sep | Room FH 6 | WeD6.2

Session: Iterative Learning Control

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.