Data-driven feedforward tuning using non-causal rational basis functions: With application to an industrial flatbed printer

Lennart Blanken; Sjirk Koekebakker; Tom Oomen

doi:10.1016/j.mechatronics.2020.102424

Data-driven feedforward tuning using non-causal rational basis functions: With application to an industrial flatbed printer

Lennart Blanken (Corresponding author), Sjirk Koekebakker, Tom Oomen

Research output: Contribution to journal › Article › Academic › peer-review

15 Citations (Scopus)

Abstract

Data-driven feedforward tuning enables high performance for control systems that perform varying tasks by using past measurement data. The aim of this paper is to develop an approach for data-driven feedforward tuning that achieves high accuracy and at the same time is computationally inexpensive. A linear parametrization is employed that enables parsimonious modeling of inverse systems for feedforward through the use of non-causal rational orthonormal basis functions in L₂. The benefits of the proposed parametrization are experimentally demonstrated on an industrial printer, including pre-actuation and cyclic pole repetition.

Original language	English
Article number	102424
Number of pages	8
Journal	Mechatronics
Volume	71
DOIs	https://doi.org/10.1016/j.mechatronics.2020.102424
Publication status	Published - Nov 2020

Funding

We would like to thank Goksan Isil for his contributions to the experiments. This work is supported by Océ Technologies , and is part of the research programme VIDI with project number 15698, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO) .

Keywords

Basis functions
Feedforward control
Learning control
Mechatronic systems
Motion control systems
Non-causal systems

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10.1016/j.mechatronics.2020.102424

Cite this

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Data-driven feedforward tuning using non-causal rational basis functions: With application to an industrial flatbed printer

Abstract

Funding

Keywords

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Data for: Data-Driven Feedforward Tuning using Non-Causal Rational Basis Functions: with Application to an Industrial Flatbed Printer

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Data-driven feedforward tuning using non-causal rational basis functions: With application to an industrial flatbed printer

Abstract

Funding

Keywords

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Data for: Data-Driven Feedforward Tuning using Non-Causal Rational Basis Functions: with Application to an Industrial Flatbed Printer

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