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@InProceedings{schirmer:icalepcs2019-wepha138,
author = {D. Schirmer},
title = {{Orbit Correction With Machine Learning Techniques at the Synchrotron Light Source DELTA}},
booktitle = {Proc. ICALEPCS'19},
pages = {1426--1430},
paper = {WEPHA138},
language = {english},
keywords = {network, storage-ring, electron, controls, synchrotron},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-WEPHA138},
url = {https://jacow.org/icalepcs2019/papers/wepha138.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA138},
abstract = {In the last years, artificial intelligence (AI) has experienced a renaissance in many fields. AI-based concepts are nature-inspired and can also be used in the field of accelerator controls. At DELTA, various studies on this subject were conducted in the past. Among other possible applications, the use of neural networks for automated correction of the electron beam position (orbit control) is of interest. Machine learning (ML) simulations with a DELTA storage ring model were already successful. Recently, conventional Feed-Forward Neural Networks (FFNN) were trained on measured orbits to apply local and global beam position corrections to the 1.5 GeV storage ring DELTA. First experimental results are presented and compared with other orbit control methods.},
}