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@inproceedings{wang:icalepcs2023-tumbcmo14, author = {F.Y. Wang and J. Cruz}, title = {{Initial Test of a Machine Learning Based SRF Cavity Active Resonance Control}}, % booktitle = {Proc. ICALEPCS'23}, booktitle = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)}, eventdate = {2023-10-09/2023-10-13}, pages = {379--381}, paper = {TUMBCMO14}, language = {english}, keywords = {cavity, controls, SRF, resonance, simulation}, venue = {Cape Town, South Africa}, series = {International Conference on Accelerator and Large Experimental Physics Control Systems}, number = {19}, publisher = {JACoW Publishing, Geneva, Switzerland}, month = {02}, year = {2024}, issn = {2226-0358}, isbn = {978-3-95450-238-7}, doi = {10.18429/JACoW-ICALEPCS2023-TUMBCMO14}, url = {https://jacow.org/icalepcs2023/papers/tumbcmo14.pdf}, abstract = {{We’ll introduce a high precision active motion controller based on machine learning (ML) technology and electric piezo actuator. The controller will be used for SRF cavity active resonance control, where a data-driven model for system motion dynamics will be developed first, and a model predictive controller (MPC) will be built accordingly. Simulation results as well as initial test results with real SRF cavities will be presented in the paper. }}, }