Data-Driven Feedforward Learning With Force Ripple Compensation for Wafer Stages: A Variable-Gain Robust Approach

IEEE Transactions on Neural Networks and Learning Systems(2022)

Cited 40|Views29
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Abstract
To meet the increasing demand for denser integrated circuits, feedforward control plays an important role in the achievement of high servo performance of wafer stages. The preexisting feedforward control methods, however, are subject to either inflexibility to reference variations or poor robustness. In this article, these deficiencies are removed by a novel variable-gain iterative feedforward tun...
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Key words
Feedforward systems,Robustness,Convergence,Servomotors,Radio frequency,Task analysis,Stochastic processes
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