1. Enhancing SRF cavity stability and minimizing detuning with data-driven resonance control based on dynamic mode decomposition.
- Author
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Wang, Faya
- Subjects
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MACHINE learning , *MOTION control devices , *RESONANCE , *GATE array circuits , *STORAGE rings , *RADIO frequency , *BREMSSTRAHLUNG - Abstract
Effective resonance control of superconducting radio frequency (SRF) cavities is critical for large machines like LCLS-II, as failure to achieve proper control can result in increased RF power consumption, higher cryogenic heat loads, and increased costs. To address this challenge, we have developed a machine learning (ML) model based on the dynamic mode decomposition method to represent the forced cavity dynamics. Using this model, we designed a model predictive controller (MPC) and demonstrated through simulation that the MPC can effectively stabilize the amplitude and phase of SRF cavities using only a frequency actuator, even in the presence of multiple mechanical modes. The lightweight and explicit ML model makes the controller suitable for direct implementation on field-programmable gate arrays, unlocking the full potential of SRF linacs like LCLS-II, enabling higher beam power and energy, and also serving as an advanced motion controller for various applications, such as photon beamlines and storage rings. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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