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.
|Number of pages||8|
|Publication status||Published - Nov 2020|
- Basis functions
- Feedforward control
- Learning control
- Mechatronic systems
- Motion control systems
- Non-causal systems
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Data for: Data-Driven Feedforward Tuning using Non-Causal Rational Basis Functions: with Application to an Industrial Flatbed Printer
Koekebakker, S. H. (Contributor), Oomen, T. A. E. (Contributor) & Blanken, L. L. G. (Contributor), Mendeley Data, 26 Apr 2021