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

M.J. van Haren; Lennart L.G. Blanken; Tom A.E. Oomen

doi:10.1016/j.ifacol.2023.10.662

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

M.J. van Haren (Corresponding author), Lennart L.G. Blanken, Tom A.E. Oomen

Onderzoeksoutput: Bijdrage aan tijdschrift › Congresartikel › peer review

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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.

Originele taal-2	Engels
Pagina's (van-tot)	6063-6068
Aantal pagina's	6
Tijdschrift	IFAC-PapersOnLine
Volume	56
Nummer van het tijdschrift	2
DOI's	https://doi.org/10.1016/j.ifacol.2023.10.662
Status	Gepubliceerd - 1 jul. 2023
Evenement	22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duur: 9 jul. 2023 → 14 jul. 2023 Congresnummer: 22 https://www.ifac2023.org/

Financiering

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.

Financiers	Financiernummer
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Horizon 2020
Electronic Components and Systems for European Leadership	101007311

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title = "A Kernel-Based Identification Approach to LPV Feedforward: With Application to Motion Systems",

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.",

keywords = "Bayesian methods, Linear parameter-varying systems, Mechatronics, Motion control systems, data-driven control",

author = "{van Haren}, M.J. and Blanken, {Lennart L.G.} and Oomen, {Tom A.E.}",

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AU - Blanken, Lennart L.G.

AU - Oomen, Tom A.E.

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

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