Full Text:   <399>

Summary:  <152>

CLC number: TP27; TH133

On-line Access: 2019-01-30

Received: 2018-09-12

Revision Accepted: 2018-12-25

Crosschecked: 2019-01-08

Cited: 0

Clicked: 892

Citations:  Bibtex RefMan EndNote GB/T7714


Zheng-tao Ding


Ze-zhi Tang


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.1 P.131-140


Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems

Author(s):  Ze-zhi Tang, Yuan-jin Yu, Zhen-hong Li, Zheng-tao Ding

Affiliation(s):  School of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, United Kingdom; more

Corresponding email(s):   zhengtao.ding@manchester.ac.uk

Key Words:  Active magnetic bearings (AMBs), Iterative learning control (ILC), Disturbance observer

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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.

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publisher="Zhejiang University Press & Springer",

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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
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800558

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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