Full Text:   <2567>

Summary:  <1839>

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

Clicked: 6326

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

-   Go to

Article info.
Open peer comments

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


Share this article to: More <<< Previous Article|

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.

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

Reference

[1]Ahn HS, Chen YQ, Moore KL, 2007. Iterative learning control: brief survey and categorization. IEEE Trans Syst Man Cybern Part C, 37(6):1099-1121.

[2]Arimoto S, Kawamura S, Miyazaki F, 1984. Bettering operation of robots by learning. J Field Robot, 1(2):123-140.

[3]Baβsler S, Dünow P, Marquardt M, et al., 2015. Application of iterative learning control methods for a service robot with multi-body kinematics. 20$^textth$ Int Conf on Methods and Models in Automation and Robotics, p.465-470.

[4]Bi C, Wu DZ, Jiang Q, et al., 2005. Automatic learning control for unbalance compensation in active magnetic bearings. IEEE Trans Magn, 41(7):2270-2280.

[5]Bleuler H, Cole M, Keogh P, et al., 2009. Magnetic Bearings: Theory, Design, and Application to Rotating Machinery. Springer-Verlag Berlin Heidelberg.

[6]Bolder J, Lemmen B, Koekebakker S, et al., 2012. Iterative learning control with basis functions for media positioning in scanning inkjet printers. IEEE Int Symp on Intelligent Control, p.1255-1260.

[7]Chen WH, Yang J, Guo L, et al., 2016. Disturbance-observer-based control and related methods—an overview. IEEE Trans Ind Electron, 63(2):1083-1095.

[8]Chladny RR, Koch CR, 2008. Flatness-based tracking of an electromechanical variable valve timing actuator with disturbance observer feedforward compensation. IEEE Trans Contr Syst Technol, 16(4):652-663.

[9]Hong SK, Langari R, 2000. Robust fuzzy control of a magnetic bearing system subject to harmonic disturbances. IEEE Trans Contr Syst Technol, 8(2):366-371.

[10]Kucera L, 1997. Robustness of self-sensing magnetic bearing. Proc Industrial Conf and Exhibition on Magnetic Bearings, p.261-270.

[11]Lee JH, Allaire PE, Tao G, et al., 2003. Experimental study of sliding mode control for a benchmark magnetic bearing system and artificial heart pump suspension. IEEE Trans Contr Syst Technol, 11(1):128-138.

[12]Li SH, Yang J, Chen WH, et al., 2012. Generalized extended state observer based control for systems with mismatched uncertainties. IEEE Trans Ind Electron, 59(12):4792-4802.

[13]Lindlau JD, Knospe CR, 2002. Feedback linearization of an active magnetic bearing with voltage control. IEEE Trans Contr Syst Technol, 10(1):21-31.

[14]Liu HX, Li SH, 2012. Speed control for PMSM servo system using predictive functional control and extended state observer. IEEE Trans Ind Electron, 59(2):1171-1183.

[15]Mandra S, Galkowski K, Aschemann H, et al., 2015. Guaranteed cost iterative learning control—an application to control of permanent magnet synchronous motors. IEEE 9$^textth$ Int Workshop on Multidimensional (nD) Systems, p.1-6.

[16]Matsumura F, Namerikawa T, Hagiwara K, et al., 1996. Application of gain scheduled H_∞ infinity robust controllers to a magnetic bearing. IEEE Trans Contr Syst Technol, 4(5):484-493.

[17]Matsumura T, Kataza H, Utsunomiya S, et al., 2016. Design and performance of a prototype polarization modulator rotational system for use in space using a superconducting magnetic bearing. IEEE Trans Appl Supercond, 26(3):3602304.

[18]Noh MD, Cho SR, Kyung JH, et al., 2005. Design and implementation of a fault-tolerant magnetic bearing system for turbo-molecular vacuum pump. IEEE/ASME Trans Mech, 10(6):626-631.

[19]Peng C, Fang JC, Xu XB, 2015. Mismatched disturbance rejection control for voltage-controlled active magnetic bearing via state-space disturbance observer. IEEE Trans Power Electron, 30(5):2753-2762.

[20]Sawada H, Hashimoto T, Ninomiya K, 2001. High-stability attitude control of satellites by magnetic bearing wheels. Trans Jpn Soc Aeronaut Space Sci, 44(145):133-141.

[21]Sun JK, Li SH, 2017. Disturbance observer based iterative learning control method for a class of systems subject to mismatched disturbances. Trans Inst Meas Contr, 39(11):1749-1760.

[22]Sun JK, Li SH, Yang J, 2014. Iterative learning control with extended state observer for iteration-varying disturbance rejection. Proc 11th World Congress on Intelligent Control and Automation, p.1148-1153.

[23]Yang J, Zheng WX, 2014. Offset-free nonlinear MPC for mismatched disturbance attenuation with application to a static var compensator. IEEE Trans Circ Syst II, 61(1):49-53.

[24]Yu YJ, Yang ZH, Fang JC, 2015. Medium-frequency disturbance attenuation for the spacecraft via virtual-gimbal tilting of the magnetically suspended reaction wheel. IET Contr Theory Appl, 9(7):1066-1074.

[25]Yu YJ, Yang ZH, Han C, et al., 2017. Active vibration control of magnetically suspended wheel using active shaft deflection. IEEE Trans Ind Electron, 64(8):6528-6537.

[26]Yu YJ, Yang ZH, Han C, et al., 2018a. Fuzzy adaptive back-stepping sliding mode controller for high-precision deflection control of the magnetically suspended momentum wheel. IEEE Trans Ind Electron, 65(4):3530-3538.

[17]Yu YJ, Yang ZH, Han C, et al., 2018b. Disturbance-observer based control for magnetically suspended wheel with synchronous noise. Contr Eng Pract, 72:83-89.

[28]Zhao YM, Lin Y, Xi FF, et al., 2015. Calibration-based iterative learning control for path tracking of industrial robots. IEEE Trans Ind Electron, 62(5):2921-2929.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE