scholarly journals Iterative Learning Based Accumulative Disturbance Observer for Repetitive Systems via a Virtual Linear Data Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 79287-79298 ◽  
Author(s):  
Yangchun Wei ◽  
Rongrong Wang ◽  
Ronghu Chi
Keyword(s):  
Data Model ◽  
Disturbance Observer ◽  
Iterative Learning ◽  
Repetitive Systems

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.

Robust iterative learning contouring controller with disturbance observer for machine tool feed drives

2018 ◽  
Vol 75 ◽  
pp. 207-215 ◽  
Author(s):  
Kenneth Renny Simba ◽  
Ba Dinh Bui ◽  
Mathew Renny Msukwa ◽  
Naoki Uchiyama
Keyword(s):  
Machine Tool ◽  
Disturbance Observer ◽  
Iterative Learning ◽  
Feed Drives
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