Gaussian Process based Feedforward Control for Nonlinear Systems with Flexible Tasks

With Application to a Printer with Friction

Journal article (2022)

Authors

Max van Meer Eindhoven University of Technology
Maurice Poot Eindhoven University of Technology
Jim Portegies Eindhoven University of Technology
T.A.E. Oomen Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering , Eindhoven University of Technology
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Max Van Meer, Maurice Poot, Jim Portegies, T.A.E. Oomen
DOI
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https://doi.org/10.1016/j.ifacol.2022.11.191
Grey box modelling Identification for control Nonlinear system identification Feedforward Control Gaussian Process regression
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Published Date

2022

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

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.