The automatic frequency control based on artificial intelligence for compact particle accelerator

In this work, an advantage actor critic (A2C) based intelligent automatic frequency control (AFC) system was developed for X-band linear accelerator (LINAC). A2C is one type of reinforcement learning, which indicates how software agents should perform actions in an environment. In this paper, the A2C based AFC algorithm and its environment design, simulation result, and controller hardware and software processes are described. The objective of our design is to match LINAC and magnetron frequency, and it is implemented via reward shaping using comparison with the reflected power in the adjacent time. The simulation with the A2C algorithm was implemented in two modes, namely, periodic and White Gaussian Noise (WGN) waves, for analysis with temperature and random disturbance, respectively. In order to create artificial disturbance in the experiment, the magnetron shaft was shifted randomly every 0.5 s by using WGN with 18° angle of the step motor. The standard deviation of the reflected power was 5.63 kW, and the average power was 130.9 kW. To obtain maximum reward at the beginning of the A2C training, the adjacent frequency is needed. The measured average reflected power and standard deviation were 122.8 kW and 1.75 kW, respectively, after 2000 iterations. The results show that the reflected power and standard deviation of the AFC with A2C were lower compared to open-loop with artificial disturbance. The RF station of a medical X-band LINAC was used as the test bench, and the performance was confirmed by the results of an experiment conducted at Sungkyunkwan University in Korea.