|MOSH4002||A Cloud Based Framework for Advanced Accelerator Controls||655|
|MOPHA038||use link to see paper's listing under its alternate paper code|
Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682.
Modern particle accelerator facilities generate large amounts of data and face increasing demands on their operational performance. As the demand on accelerator operations increases so does the need for automated tuning algorithms and control to maximize uptime with reduced operator intervention. Existing tools are insufficient to meet the broad demands on controls, visualization, and analysis. We are developing a cloud based toolbox featuring a generic virtual accelerator control room for the development of automated tuning algorithms and the analysis of large complex datasets. This framework utilizes tracking codes combined with with algorithms for machine drift, low-level control systems, and other complications to create realistic models of accelerators. These models are directly interfaced with advanced control toolboxes allowing for rapid prototyping of control algorithms. Additionally, our interface provides users with access to a wide range of Python-based data analytics libraries for the study and visualization of machine data. In this paper, we provide an overview of our interface and demonstrate its utility on a toy accelerator running on EPICS.
|DOI •||reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOSH4002|
|About •||paper received ※ 30 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020|
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