Xuewei Fu
An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to A Linear Motor
Fu, Xuewei; Yang, Xiaofeng; Zanchetta, Pericle; Tang, Mi; Liu, Yang; Chen, Zhenyu
Authors
Xiaofeng Yang
PERICLE ZANCHETTA pericle.zanchetta@nottingham.ac.uk
Professor of Control Engineering
Mi Tang
Yang Liu
Zhenyu Chen
Abstract
The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both removing the need for prior knowledge of the system plant in model-based feedforward and improving the extrapolation capability for varying tasks of iterative learning control. However, most of IFFT methods require to set the number of basis functions in advance, which is inconvenient to the system design. To tackle this problem, an adaptive data-driven IFFT based on fast recursive algorithm (IFFT-FRA) is developed in this paper. Explicitly, based on FRA the proposed approach can adaptively tune the feedforward structure, which significantly increases the intelligence of the approach. Additionally, a data-based iterative tuning procedure is introduced to achieve the unbiased estimation of parameters optimization in presence of noise. Comparative experiments on a linear motor confirms the effectiveness of the proposed approach.
Citation
Fu, X., Yang, X., Zanchetta, P., Tang, M., Liu, Y., & Chen, Z. (2023). An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to A Linear Motor. IEEE Transactions on Industrial Informatics, 19(4), 6160-6169. https://doi.org/10.1109/tii.2022.3202818
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 21, 2022 |
Online Publication Date | Aug 30, 2022 |
Publication Date | 2023-04 |
Deposit Date | Jan 6, 2023 |
Publicly Available Date | Mar 29, 2024 |
Journal | IEEE Transactions on Industrial Informatics |
Print ISSN | 1551-3203 |
Electronic ISSN | 1941-0050 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Volume | 19 |
Issue | 4 |
Pages | 6160-6169 |
DOI | https://doi.org/10.1109/tii.2022.3202818 |
Keywords | Electrical and Electronic Engineering; Computer Science Applications; Information Systems; Control and Systems Engineering |
Public URL | https://nottingham-repository.worktribe.com/output/10638862 |
Publisher URL | https://ieeexplore.ieee.org/document/9870555 |
Files
Data-Driven Iterative Feedforward Tuning
(2.8 Mb)
PDF
You might also like
Direct Model Predictive Control of Synchronous Reluctance Motor Drives
(2022)
Journal Article
Analysis and Fault-Tolerant Control for Dual-Three-Phase PMSM Based on Virtual Healthy Model
(2022)
Journal Article
Power Electronics Converters for the Internet of Energy: A Review
(2022)
Journal Article
Reconfigurable Cascaded Multilevel Converter design for Battery Energy System Storage
(2022)
Conference Proceeding
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: digital-library-support@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search