A Kernel-Based Identification Approach to LPV Feedforward: With Application to Motion Systems

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Abstract

The increasing demands for motion control result in a situation where Linear Parameter-Varying (LPV) dynamics have to be taken into account. Inverse-model feedforward control for LPV motion systems is challenging, since the inverse of an LPV system is often dynamically dependent on the scheduling sequence. The aim of this paper is to develop an identification approach that directly identifies dynamically scheduled feedforward controllers for LPV motion systems from data. In this paper, the feedforward controller is parameterized in basis functions, similar to, e.g., mass-acceleration feedforward, and is identified by a kernel-based approach such that the parameter dependency for LPV motion systems is addressed. The resulting feedforward includes dynamic dependence and is learned accurately. The developed framework is validated on an example.
Original languageEnglish
Pages (from-to)6063-6068
Number of pages6
JournalIFAC-PapersOnLine
Volume56
Issue number2
DOIs
Publication statusPublished - 1 Jul 2023
Event22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023
Conference number: 22
https://www.ifac2023.org/

Funding

This work is part of the research programme VIDI with project number 15698, which is (partly) financed by the Netherlands Or-ganisation for Scientific Research (NWO). In addition, this research has received funding from the ECSEL Joint Undertaking under grant agreement 101007311 (IMOCO4.E). The Joint Undertaking receives support from the European Union Horizon 2020 research and innovation programme.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
European Union's Horizon 2020 - Research and Innovation Framework Programme
Electronic Components and Systems for European Leadership101007311

    Keywords

    • Bayesian methods
    • Linear parameter-varying systems
    • Mechatronics
    • Motion control systems
    • data-driven control

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