Application of a modified iterative learning control algorithm for superconducting radio-frequency cavities - Xu, Chengye; Zhu, Zhenglong; Qiu, Feng; Jiang, Tiancai; Chen, Qi; Gao, Zheng; Ma, Jinying; Shi, Longbo; Jiang, Guodong; Xue, Zongheng; Jin, Kean; Sun, Liepeng; et al. (Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, 2022)
Transient beam loading, which causes cavity gradient fluctuation, is becoming a major concern for the stable operation of the high current superconducting radio-frequency (SRF) accelerators. Iterative learning control (ILC) is an effective algorithm aiming to improve systems operated in repetitive mode. This ILC technique was successfully introduced to the low-level radio-frequency (LLRF) control in accelerators to compensate for the field fluctuation caused by repetitively pulsed beam. The modern LLRF system prefers to use the FPGA-based hardware platform to realize a real-time control framework. However, considering the algorithm complexity and the hardware cost, the ILC algorithm is usually implemented outside FPGA. This practice would decrease the real-time ability of the control system. In this paper, we present a modified ILC algorithm that can be implemented inside FPGA. The key idea of our method is to simplify the beam profile using a rectangular pulse. The method was demonstrated in the SRF cavities at Chinese Accelerator driven system Front-end demo SRF linac (CAFe). The experimental results in the CAFe beam-commissioning confirmed that the beam-induced gradient fluctuation is successfully suppressed.