The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
@inproceedings{kafkes:ipac2021-tupab327,
author = {D.L. Kafkes and M. Schram},
title = {{Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster}},
booktitle = {Proc. IPAC'21},
pages = {2268--2271},
eid = {TUPAB327},
language = {english},
keywords = {controls, network, booster, power-supply, FPGA},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-TUPAB327},
url = {https://jacow.org/ipac2021/papers/tupab327.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-TUPAB327},
abstract = {{We describe the offline machine learning (ML) development for an effort to precisely regulate the Gradient Magnet Power Supply (GMPS) at the Fermilab Booster accelerator complex via a Field-Programmable Gate Array (FPGA). As part of this effort, we created a digital twin of the Booster-GMPS control system by training a Long Short-Term Memory (LSTM) to capture its full dynamics. We outline the path we took to carefully validate our digital twin before deploying it as a reinforcement learning (RL) environment. Additionally, we demonstrate the use of a Deep Q-Network (DQN) policy model with the capability to regulate the GMPS against realistic time-varying perturbations.}},
}