RadiaSoft LLC, Boulder, Colorado, USA
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.
RadiaSoft LLC, Boulder, Colorado, USA
BNL, Upton, New York, USA
The Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory will undergo a beam energy scan over the next several years. To execute this scan, the transfer line between the Alternating Gradient Synchrotron (AGS) and RHIC or the so-called the ATR line, must be re-tuned for each energy. Control of the ATR line has four primary constraints: match the beam trajectory into RHIC, match the transverse focusing, match the dispersion, and minimize losses. Some of these can be handled independently, for example orbit matching. However, offsets in the beam can affect the transverse beam optics, thereby coupling the dynamics. Furthermore, the introduction of vertical optics increases the possibilities for coupling between transverse planes, and the desire to make the line spin transparent further complicates matters. During this talk, we will explore three promising avenues for controlling the ATR line, model predictive control (MPC), on-line optimization methods, and hybrid MPC and optimization methods. We will provide an overview of each method, discuss the tradeoffs between these methods, and summarize our conclusions.
