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

Ioannis Proimadis, Yorick Broens, Roland Tóth, Hans Butler
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:535-546, 2021.

Abstract

Growing demands in the semiconductor industry result in the need for enhanced performance of lithographic equipment. However, position tracking accuracy of high precision mechatronics is often limited by the presence of disturbance sources, which originate from unmodelled or unforeseen deterministic environmental effects. To negate the effects of these disturbances, a learning based feedforward controller is employed, where the underlying control policy is estimated from experimental data based on Gaussian Process regression. The proposed approach exploits the property of including prior knowledge on the expected steady state behaviour of residual dynamics in terms of kernel selection. Corresponding hyper-parameters are optimized using the maximization of the marginalized likelihood. Consequently, the learned function is employed as augmentation of the currently employed rigid body feedforward controller. The effectiveness of the augmentation is experimentally validated on a magnetically levitated planar motor stage. The results of this paper highlight the benefits and possibilities of machine-learning based approaches for compensation of static effects, which originate from residual dynamics, such that position tracking performance for moving-magnet planar motor actuators is improved.

Cite this Paper

BibTeX
@InProceedings{pmlr-v144-proimadis21a, title = {Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system}, author = {Proimadis, Ioannis and Broens, Yorick and T\'oth, Roland and Butler, Hans}, booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control}, pages = {535--546}, year = {2021}, editor = {Jadbabaie, Ali and Lygeros, John and Pappas, George J. and A. Parrilo, Pablo and Recht, Benjamin and Tomlin, Claire J. and Zeilinger, Melanie N.}, volume = {144}, series = {Proceedings of Machine Learning Research}, month = {07 -- 08 June}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v144/proimadis21a/proimadis21a.pdf}, url = {https://proceedings.mlr.press/v144/proimadis21a.html}, abstract = {Growing demands in the semiconductor industry result in the need for enhanced performance of lithographic equipment. However, position tracking accuracy of high precision mechatronics is often limited by the presence of disturbance sources, which originate from unmodelled or unforeseen deterministic environmental effects. To negate the effects of these disturbances, a learning based feedforward controller is employed, where the underlying control policy is estimated from experimental data based on Gaussian Process regression. The proposed approach exploits the property of including prior knowledge on the expected steady state behaviour of residual dynamics in terms of kernel selection. Corresponding hyper-parameters are optimized using the maximization of the marginalized likelihood. Consequently, the learned function is employed as augmentation of the currently employed rigid body feedforward controller. The effectiveness of the augmentation is experimentally validated on a magnetically levitated planar motor stage. The results of this paper highlight the benefits and possibilities of machine-learning based approaches for compensation of static effects, which originate from residual dynamics, such that position tracking performance for moving-magnet planar motor actuators is improved.} }
Endnote
%0 Conference Paper %T Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system %A Ioannis Proimadis %A Yorick Broens %A Roland Tóth %A Hans Butler %B Proceedings of the 3rd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2021 %E Ali Jadbabaie %E John Lygeros %E George J. Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire J. Tomlin %E Melanie N. Zeilinger %F pmlr-v144-proimadis21a %I PMLR %P 535--546 %U https://proceedings.mlr.press/v144/proimadis21a.html %V 144 %X Growing demands in the semiconductor industry result in the need for enhanced performance of lithographic equipment. However, position tracking accuracy of high precision mechatronics is often limited by the presence of disturbance sources, which originate from unmodelled or unforeseen deterministic environmental effects. To negate the effects of these disturbances, a learning based feedforward controller is employed, where the underlying control policy is estimated from experimental data based on Gaussian Process regression. The proposed approach exploits the property of including prior knowledge on the expected steady state behaviour of residual dynamics in terms of kernel selection. Corresponding hyper-parameters are optimized using the maximization of the marginalized likelihood. Consequently, the learned function is employed as augmentation of the currently employed rigid body feedforward controller. The effectiveness of the augmentation is experimentally validated on a magnetically levitated planar motor stage. The results of this paper highlight the benefits and possibilities of machine-learning based approaches for compensation of static effects, which originate from residual dynamics, such that position tracking performance for moving-magnet planar motor actuators is improved.
APA
Proimadis, I., Broens, Y., Tóth, R. & Butler, H.. (2021). Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system. Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research 144:535-546 Available from https://proceedings.mlr.press/v144/proimadis21a.html.

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