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

被引：0

作者：

van Hulst, Jilles ^{[1
]}

Poot, Maurice ^{[1
]}

Kostic, Dragan ^{[2
]}

Yan, Kai Wa ^{[2
]}

Portegies, Jim ^{[3
]}

Oomen, Tom ^{[1
,4
]}

机构：

[1] Eindhoven Univ Technol, Control Syst Technol Sect, Dept Mech Engn, Eindhoven, Netherlands

[2] ASM Pacific Technol, Ctr Competency, Eindhoven, Netherlands

[3] Eindhoven Univ Technol, CASA, Dept Math & Comp Sci, Eindhoven, Netherlands

[4] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands

来源：

IFAC PAPERSONLINE | 2022年 / 55卷 / 37期

关键词：

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

D O I：

10.1016/jifacol.2022.11.190

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

引用

页码：235 / 240

页数：6

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