From Batch-to-Batch to online learning control: experimental motion control case study

Noud Mooren (Corresponding author), Gert Witvoet (Corresponding author), Tom Oomen (Corresponding author)

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Data-driven feedforward control can significantly improve the positioning performance of motion systems. The aim of this paper is to exploit the concept of batch-to-batch learning control with basis function, applied in an online fashion. This enables learning within a task while maintaining task flexibility. A recursive least squares optimization is proposed on the basis of input/output data to compute the optimal feedforward parameters. The proposed method is successfully validated in simulation, and applied to a benchmark motion system leading to a major performance improvement compared to only feedback control.
Original languageEnglish
Pages (from-to)406-411
Number of pages6
Issue number15
Publication statusPublished - Sep 2019
Event8th IFAC Symposium on Mechatronic Systems, MECHATRONICS 2019 - Vienna, Austria
Duration: 4 Sep 20196 Sep 2019


  • Feedforward control
  • Learning control
  • Parameter estimation
  • Feedforward Control
  • Parameter Estimation
  • Learning Control

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