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RIS citation export for THAL03: Machine Learning Based Middle-Layer for Autonomous Accelerator Operation and Control

AU  - Pioli, S.
AU  - Buonomo, B.
AU  - Cardelli, F.
AU  - Ciuffetti, P.
AU  - Di Giovenale, D.
AU  - Di Giulio, C.
AU  - Foggetta, L.G.
AU  - Martinelli, V.
AU  - Piermarini, G.
ED  - Furukawa, Kazuro
ED  - Yan, Yingbing
ED  - Leng, Yongbin
ED  - Chen, Zhichu
ED  - Schaa, Volker R.W.
TI  - Machine Learning Based Middle-Layer for Autonomous Accelerator Operation and Control
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  - The Singularity project, led by National Laboratories of Frascati of the National Institute for Nuclear Physics (INFN-LNF), aim to develop automated machine-independent middle-layer to control accelerator operation through machine learning (ML) algorithms like Reinforcement Learning (RL) and Cluster integrated with accelerator’s sub-systems. In this work we will present architecture and of the middle-layer made with main purpose to drive user requests through the control framework backend and allow users to enjoy a better User Experience (UX) handling system performances without facing problems due to the interaction with control system. We will report the strategy to develop autonomous operation control with RL algorithms together with the fault detection capability improved by Clustering approach as breakdown and waveguide and RF cavity thermal stability monitor. Results of the first period of operation of this system, currently operating at the electron-positron LINAC of the Dafne complex in Frascati, autonomously controlling accelerator performance in terms of beam transport, beam current optimization and RF cavity phase-jitter compensation will be reported.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 797
EP  - 802
KW  - controls
KW  - linac
KW  - operation
KW  - vacuum
KW  - gun
DA  - 2022/03
PY  - 2022
SN  - 2226-0358
SN  - 978-3-95450-221-9
DO  - doi:10.18429/JACoW-ICALEPCS2021-THAL03
UR  - https://jacow.org/icalepcs2021/papers/thal03.pdf
ER  -