Oct 9 – 13, 2022
FHNW Campus Brugg-Windisch
Europe/Zurich timezone

MACHINE LEARNING BASED SRF CAVITY ACTIVE RESONANCE CONTROL

Oct 10, 2022, 1:45 PM
30m
Aula (Building 3)

Aula

Building 3

Oral Low Level RF Workshop 2022 Machine Learning

Speaker

Faya Wang

Description

Motion control for acceleration system is usually very complex, as beam and electromagnetic field may couple with mechanical energy. For example, in SRF cavity, electromagnetic modes are strongly coupled with its mechanical modes via Lorentz-force detuning or external microphonics. Since the coupling is very nonlinear, motion control is usually very challenging, such as resonance control of SRF cavity. We propose to develop a high precision active motion controller based on machine learning (ML) technology and electric piezo actuator. We’ll first develop a data-driven model for system motion dynamics, and then develop a model predictive controller (MPC). Finally, the performance of the controller will be verified on a real machine. For the technology demonstration, we’ll apply the technology for SRF cavity resonance control on LCLS-II SRF linac, as it is a great test bed for challenging motion control problem.

Primary author

Presentation materials