Combined disturbance-observer-based control and iterative learning control design for pulsed superconducting radio frequency cavities

2021 ◽  
Vol 32 (6) ◽  
Author(s):  
Feng Qiu ◽  
Shinichiro Michizono ◽  
Toshihiro Matsumoto ◽  
Takako Miura
Keyword(s):  
Radio Frequency ◽  
Iterative Learning Control ◽  
Disturbance Observer ◽  
Control Design ◽  
Learning Control ◽  
Iterative Learning ◽  
Superconducting Radio Frequency

Passivity based stabilization of repetitive processes and iterative learning control design

2018 ◽  
Vol 122 ◽  
pp. 101-108 ◽  
Author(s):  
Pavel Pakshin ◽  
Julia Emelianova ◽  
Mikhail Emelianov ◽  
Krzysztof Galkowski ◽  
Eric Rogers
Keyword(s):  
Iterative Learning Control ◽  
Control Design ◽  
Learning Control ◽  
Iterative Learning ◽  
Repetitive Processes

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.

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