Research On The Beam Automatic Modulating And Control System For SFC Injection Beam Line By Artificial Neural Network

The traditional beam tuning method for HIRFL-CSR which is based on manual debugging is very complicated, and this method not only needs lots of manpower and material resources but also difficult to achieve stable control aim. To resolve this problem, a neural network-based beam automatic modulating control system is proposed and two hardware platforms are designed for SFC injection beam line in this thesis.According the beam tuning characteristics, the beam automatic modulating control system of HIRFL-CSR is designed into two layers:the intelligent beam tuning decision layer and power supply adjustment layer. The intelligent beam tuning decision layer collects and analyses the parameters from the feedback of beam diagnostic elements, then gives the power supply parameters. The main role of power supply adjustment layer is to receive the power parameters from the intelligent beam tuning decision layer, and then adjust the parameters by using optimization algorithm. The.net industrial control computer is designed for communication between the two layers, its function is network switching and industrial control, and has the advantages of intelligent network switches and industrial computer. The μPAC is designed for power adjustment layer by using the structure of FPGA+ARM. ARM is designed as the core processor, and FPGA is designed for power digital adjust algorithm.In order to solve the communications blockage problem of the power supply controller in Lanzhou Heavy Ion Accelerator CSR, a new system architecture is adopted, P2P technology is embedded to the core processor ARM of the μPAC. Based on the technology of UDP holing, a direct communication connect is established between the controllers, so the data no longer forwarded by the main Oracle database system acting as a server. This method decentralized the communication responsibility of Oracle database system, the risk of network block is reduced, and the effectiveness and reliability of communication is improved. At the same time, beam automatic modulating control system has the need of strong real-time and parallel processing, so BP neural network is optimized in the FPGA, and the control system is simulated by the tools boxs of Simlulink in MATLAB.