Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach

Jilles Van Hulst; Maurice Poot; Dragan Kostic; Kai Wa Yan; Jim Portegies; Tom Oomen

doi:10.1016/j.ifacol.2022.11.190

Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach

Jilles Van Hulst, Maurice Poot, Dragan Kostic, Kai Wa Yan, Jim Portegies, Tom Oomen

Team Jan-Willem van Wingerden

Research output: Contribution to journal › Conference article › Scientific › peer-review

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Abstract

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.

Original language	English
Pages (from-to)	235-240
Journal	IFAC-PapersOnline
Volume	55
Issue number	37
DOIs	https://doi.org/10.1016/j.ifacol.2022.11.190
Publication status	Published - 2022
Event	2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States Duration: 2 Oct 2022 → 5 Oct 2022

Keywords

Applications in semiconductor manufacturing
Data-based control
Identification for control
Iterative learning control
Motion Control
Nonlinear system identification

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10.1016/j.ifacol.2022.11.190

1-s2.0-S2405896322028336-mainFinal published version, 608 KBLicence: CC BY-NC-ND

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title = "Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach",

abstract = "Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.",

keywords = "Applications in semiconductor manufacturing, Data-based control, Identification for control, Iterative learning control, Motion Control, Nonlinear system identification",

author = "{Van Hulst}, Jilles and Maurice Poot and Dragan Kostic and Yan, {Kai Wa} and Jim Portegies and Tom Oomen",

year = "2022",

doi = "10.1016/j.ifacol.2022.11.190",

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T2 - 2nd Modeling, Estimation and Control Conference, MECC 2022

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AU - Poot, Maurice

AU - Kostic, Dragan

AU - Yan, Kai Wa

AU - Portegies, Jim

AU - Oomen, Tom

PY - 2022

Y1 - 2022

N2 - Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.

AB - Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.

KW - Applications in semiconductor manufacturing

KW - Data-based control

KW - Identification for control

KW - Iterative learning control

KW - Motion Control

KW - Nonlinear system identification

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VL - 55

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EP - 240

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 1474-6670

IS - 37

Y2 - 2 October 2022 through 5 October 2022

ER -

Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach

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