Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system - researchr publication authors

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Ioannis Proimadis, Yorick Broens, Roland Tóth, Hans Butler. Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system. In Ali Jadbabaie, John Lygeros, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, Melanie N. Zeilinger, editors, Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, L4DC 2021, 7-8 June 2021, Virtual Event, Switzerland. Volume 144 of Proceedings of Machine Learning Research, pages 535-546, PMLR, 2021. [doi]

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