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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

 ORCID:

Zheng-tao Ding

https://orcid.org/0000-0003-0690-7853

Ze-zhi Tang

https://orcid.org/0000-0002-0182-6010

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

http://doi.org/10.1631/FITEE.1800558


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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volume="20",
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year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800558"
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T1 - Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems
A1 - Ze-zhi Tang
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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.

针对主动磁悬浮轴承干扰抑制的一类结合迭代学习控制与干扰观测器的解决方法

摘要:针对主动磁悬浮轴承系统,传统迭代学习控制可实现高精度轨迹跟踪,但系统扰动必须限定为不随迭代变化。基于目前方法,提出一种抑制主动磁悬浮轴承系统中随迭代变化的不匹配扰动方法。在该方案中,结合经典迭代学习控制和普适性扩张观测器,在使用输出反馈信息情况下,可在每次迭代过程中估计并抑制外界变化干扰。分析证明了整个闭环系统的收敛性,同时,仿真结果表明,相比传统迭代学习控制,该控制方法轨迹跟踪性能更加优良。

关键词:主动磁悬浮轴承;迭代学习控制;干扰观测器

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

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