CLC number: TP27; TH133
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-01-08
Cited: 0
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Citations: Bibtex RefMan EndNote GB/T7714
Ze-zhi Tang, Yuan-jin Yu, Zhen-hong Li, Zheng-tao Ding. Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(1): 131-140.
@article{title="Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems",
author="Ze-zhi Tang, Yuan-jin Yu, Zhen-hong Li, Zheng-tao Ding",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="1",
pages="131-140",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800558"
}
%0 Journal Article
%T Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems
%A Ze-zhi Tang
%A Yuan-jin Yu
%A Zhen-hong Li
%A Zheng-tao Ding
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 1
%P 131-140
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800558
TY - JOUR
T1 - Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems
A1 - Ze-zhi Tang
A1 - Yuan-jin Yu
A1 - Zhen-hong Li
A1 - Zheng-tao Ding
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 1
SP - 131
EP - 140
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800558
Abstract: Although standard iterative learning control (ILC) approaches can achieve perfect tracking for active magnetic bearing (AMB) systems under external disturbances, the disturbances are required to be iteration-invariant. In contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variant disturbances that are in different channels from the control inputs. A disturbance observer based ILC scheme is proposed that consists of a universal extended state observer (ESO) and a classical ILC law. Using only output feedback, the proposed control approach estimates and attenuates the disturbances in every iteration. The convergence of the closed-loop system is guaranteed by analyzing the contraction behavior of the tracking error. Simulation and comparison studies demonstrate the superior tracking performance of the proposed control approach.
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