Gaussian Process based Feedforward Control for Nonlinear Systems with Flexible Tasks

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

Van Meer, Max (Eindhoven University of Technology)
Poot, Maurice (Eindhoven University of Technology)
Portegies, Jim (Eindhoven University of Technology)
Oomen, T.A.E. (TU Delft Team Jan-Willem van Wingerden; Eindhoven University of Technology)

2022

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.

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

http://resolver.tudelft.nl/uuid:0843a9a2-b912-4849-8b72-ce4f541ca259

https://doi.org/10.1016/j.ifacol.2022.11.191

1474-6670

IFAC-PapersOnLine, 55 (37), 241-246

2nd Modeling, Estimation and Control Conference, MECC 2022, 2022-10-02 → 2022-10-05, Jersey City, United States

Institutional Repository

journal article

© 2022 Max Van Meer, Maurice Poot, Jim Portegies, T.A.E. Oomen