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RIS citation export for WEPV008: Online Automatic Optimization of the Elettra Synchrotron

AU  - Gaio, G.
AU  - Krecic, S.
AU  - Tripaldi, F.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Online Automatic Optimization of the Elettra Synchrotron
J2  - Proc. of ICALEPCS2021, Shanghai, China, 14-22 October 2021
CY  - Shanghai, China
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 18
LA  - english
AB  - Online automatic optimization is a common practice in particle accelerators. Beside the tryouts based on Machine Learning, which are effective especially on non-linear systems and images but are very complex to tune and manage, one of the most simple and robust algorithms, the simplex Nelder Mead, is extensively used at Elettra to automatically optimize the synchrotron parameters. It is currently applied to optimize the efficiency of the booster injector by tuning the pre-injector energy, the trajectory and optics of the transfer lines, and the injection system of the storage ring. It has also been applied to maximize the intensity of the photon beam on a beamline by changing the electron beam position and angle inside the undulator. The optimization algorithm has been embedded in a TANGO device that also implements generic and configurable multi-input multi-output feedback systems. This optimization tool is usually included in a high level automation framework based on behavior trees in charge of the whole process of machine preparation for the experiments.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 636
EP  - 639
KW  - feedback
KW  - booster
KW  - controls
KW  - experiment
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-WEPV008
UR  - https://jacow.org/icalepcs2021/papers/wepv008.pdf
ER  -