Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities

Superconducting Radio-Frequency (SRF) cavities operating in continuous wave (CW) mode and with low beam loading are devices characterized by a high loaded quality factor, in the order of 10^7, and narrow bandwidth, in the order of 10 Hz. The Low Level RF (LLRF) and resonance control systems of SRF cavities become a fundamental component of the entire system operation and in general has very tight stability requirements on the amplitude, phase and resonance frequency of the cavity. Microphonics plays an important role in cavity detuning, which results in more power needed to achieve the desired gradient. Active control approaches to reduce detuning in SRF cavities using piezoelectric actuators have shown promising results. Furthermore, Machine Learning (ML) techniques have also shown important capabilities to improve existing PID controllers. Specifically, Neural Networks (NN) can be used to find optimal PID gains and improve performance of traditional control systems. In this research, we develop new approaches to improve existing LLRF and resonance control systems with ML as a tool to find the optimal gains. We investigate these approaches using the LLRF control system intended for LCLS-II.
Comment: Talk and Poster presented at LLRF Workshop 2019 (LLRF2019, arXiv:1909.06754)