Machine Learning Based Middle-Layer for Autonomous Accelerator Operation and Control

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.
Proceedings of the 18th International Conference on Accelerator and Large Experimental Physics Control Systems, ICALEPCS2021, Shanghai, China