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: 6327
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.
[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>