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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

An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to A Linear Motor Thumbnail


Authors

Xuewei Fu

Xiaofeng Yang

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 Jan 6, 2023
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

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