.png){kind=icon}
.png){kind=icon}
Return
Research on LLRF Feedback Control Algorithm Based on Reinforcement Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TNS.2025.3604518.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study proposes a low-level radio frequency (LLRF) feedback control algorithm based on reinforcement learning (RL) using the soft actor-critic (SAC) and proximal policy optimization (PPO) algorithms. These approaches can automatically learn optimal control strategies for multi-input-multi-output (MIMO) systems, thereby reducing interference among variables during closed-loop control and enhancing the overall control precision of the system. To avoid interruption to accelerators when training the RL models, we modeled the relationship between LLRF operator input and klystron output to enable offline training, significantly accelerating the training. Trained RL models were validated at the FELiChEM facility. Experimental results demonstrated that the LLRF control algorithm based on RL shows improvements in both amplitude and phase closed-loop accuracy compared to the traditional univariate proportional-integral (PI) controller.
Keywords:
Training
Klystrons
Reinforcement learning
Feedback control
Thermal stability
Engines
Microwave oscillators
Microwave amplifiers
Fitting
Stability criteria
Artificial intelligence
feedback control
low-level radio frequency (LLRF)
reinforcement learning (RL)
Journal
I
IF:
1.9
Papers:
324
Citations:
1.4W