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

2019 ◽  
Vol 20 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Ze-zhi Tang ◽  
Yuan-jin Yu ◽  
Zhen-hong Li ◽  
Zheng-tao Ding
Keyword(s):  
Disturbance Rejection ◽  
Iterative Learning Control ◽  
Disturbance Observer ◽  
Learning Control ◽  
Magnetic Bearing ◽  
Iterative Learning ◽  
Active Magnetic Bearing

scholarly journals Application of the Dynamic Iterative Learning Control to the Heteroplanar Active Magnetic Bearing

2020 ◽  
Vol 53 (2) ◽  
pp. 1511-1516
Author(s):  
Lukasz Hladowski ◽  
Arkadiusz Mystkowski ◽  
Krzysztof Galkowski ◽  
Eric Rogers ◽  
Bing Chu
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Magnetic Bearing ◽  
Iterative Learning ◽  
Active Magnetic Bearing

scholarly journals A novel iterative learning control method and control system design for active magnetic bearing rotor imbalance of primary helium circulator in high-temperature gas-cooled reactor

2020 ◽  
Vol 53 (3-4) ◽  
pp. 474-484
Author(s):  
Yangbo Zheng ◽  
Xingnan Liu ◽  
Jingjing Zhao ◽  
Ni Mo ◽  
Zhengang Shi
Keyword(s):  
Control System ◽  
High Temperature ◽  
Iterative Learning Control ◽  
Control Method ◽  
Learning Control ◽  
Magnetic Bearing ◽  
Iterative Learning ◽  
Active Magnetic Bearing ◽  
Rotor Imbalance ◽  
High Temperature Gas

As one of the key technologies of high-temperature gas-cooled reactor, primary helium circulator–equipped active magnetic bearing provides driving force for primary helium cooling system. However, repetitive periodic vibration produced by rotor imbalance may introduce risks to primary helium circulator (even for high-temperature gas-cooled reactors). First, this article analyzes a periodic component extraction algorithm which is widely used in active magnetic bearing rotor unbalance control methods and points out the problem that the periodic component extraction algorithm occupies numerous computing resources which cannot satisfy the real-time request of active magnetic bearing control system. Then, a novel iterative learning control algorithm based on the iteration before last iteration of system information (iterative learning control-2) and a plug-in parallel control mechanism based on the existing control system are put forward, meanwhile, an integrated independent distributed active magnetic bearing control system is designed to solve the problem. Finally, both the simulation and experiment are carried out, respectively. The corresponding results show that the control method and control system proposed in this article have significant suppression effect on the repetitive periodic vibration of the active magnetic bearing system without degrading the real-time requirement and can provide important technical support for the safe and stable operation of the primary helium circulator in high-temperature gas-cooled reactor.

scholarly journals A New Linear Motor Force Ripple Compensation Method Based on Inverse Model Iterative Learning and Robust Disturbance Observer

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Xuewei Fu ◽  
Xiaofeng Yang ◽  
Zhenyu Chen
Keyword(s):  
Iterative Learning Control ◽  
Disturbance Observer ◽  
Learning Control ◽  
Inverse Model ◽  
Iterative Learning ◽  
Compensation Method ◽  
Tracking Accuracy ◽  
Linear Motors ◽  
Learning Speed ◽  
Force Ripple

Permanent magnet linear motors (PMLMs) are gaining increasing interest in ultra-precision and long stroke motion stage, such as reticle and wafer stage of scanner for semiconductor lithography. However, the performances of PMLM are greatly affected by inherent force ripple. A number of compensation methods have been studied to solve its influence to the system precision. However, aiming at some application, the system characteristics limit the design of controller. In this paper, a new compensation strategy based on the inverse model iterative learning control and robust disturbance observer is proposed to suppress the influence of force ripple. The proposed compensation method makes fully use of not only achievable high tracking accuracy of the inverse model iterative learning control but also the higher robustness and better iterative learning speed by using robust disturbance observer. Simulation and experiments verify effectiveness and superiority of the proposed method.

Disturbance observer based iterative learning control method for a class of systems subject to mismatched disturbances

2016 ◽  
Vol 39 (11) ◽  
pp. 1749-1760 ◽  
Author(s):  
Jiankun Sun ◽  
Shihua Li
Keyword(s):  
Iterative Learning Control ◽  
Disturbance Observer ◽  
Control Method ◽  
Simulation Analysis ◽  
Permanent Magnet Synchronous Motor ◽  
Learning Control ◽  
Iterative Learning ◽  
Closed Loop System ◽  
Practical Engineering ◽  
Mismatched Disturbances

This paper develops a systematic iterative learning control (ILC) strategy for systems with mismatched disturbances. The systems with mismatched disturbances are more general and widely exist in practical engineering, where the standard disturbance observer based ILC method is no longer available. To this end, this note proposes a novel ILC scheme based on the disturbance observer, which consists of two parts: a baseline ILC term for stabilizing the nominal system and a disturbance compensation term for attenuating mismatched disturbances by choosing an appropriate compensation gain. It is proven that the performance of the closed-loop system is effectively improved. Finally, the simulation analysis for a permanent-magnet synchronous motor servo system demonstrates the feasibility and efficacy of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document