Gaussian Process based Feedforward Control for Nonlinear Systems with Flexible Tasks: With Application to a Printer with Friction

Max Van Meer, Maurice Poot, Jim Portegies, Tom Oomen

Research output: Contribution to journalConference articleScientificpeer-review

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Abstract

Feedforward control is essential to achieving good tracking performance in positioning systems. The aim of this paper is to develop an identification strategy for inverse models of systems with nonlinear dynamics of unknown structure using input-output data, which can be used to generate feedforward signals for a-priori unknown tasks. To this end, inverse systems are regarded as noncausal nonlinear finite impulse response (NFIR) systems, and modeled as a Gaussian Process with a stationary kernel function that imposes properties such as smoothness. The approach is validated experimentally on a consumer printer with friction and shown to lead to improved tracking performance with respect to linear feedforward.

Original languageEnglish
Pages (from-to)241-246
JournalIFAC-PapersOnline
Volume55
Issue number37
DOIs
Publication statusPublished - 2022
Event2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States
Duration: 2 Oct 20225 Oct 2022

Keywords

  • Feedforward Control
  • Gaussian Process regression
  • Grey box modelling
  • Identification for control
  • Nonlinear system identification

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