Predictive ion source control using artificial neural network for RFT-30 cyclotron

An RFT-30 cyclotron is a 30 MeV proton accelerator for radioisotope production and fundamental research. The ion source of the RFT-30 cyclotron creates plasma from hydrogen gas and transports an ion beam into the center region of the cyclotron. Ion source control is used to search source parameters for best quality of the ion beam. Ion source control in a real system is a difficult and time consuming task, and the operator should search the source parameters by manipulating the cyclotron directly. In this paper, we propose an artificial neural network based predictive control approach for the RFT-30 ion source. The proposed approach constructs the ion source model by using an artificial neural network and finds the optimized parameters with the simulated annealing algorithm. To analyze the performance of the proposed approach, we evaluated the simulations with the experimental data of the ion source. The performance results show that the proposed approach can provide an efficient way to analyze and control the ion source of the RFT-30 cyclotron.