基于 Simulink 仿真的电子加速器控制方法研究

Electron accelerators are widely used in material modification, disinfection and sterilization, sewage treatment and other fields. However, in practical applications, the control of electron accelerator beam intensity can’t be adjusted quickly and accurately, which greatly reduces the efficiency and quality of production and processing. This paper aims to solve the problems of nonlinearity, time-varying and instability in the beam control process of electron accelerator. To achieve this, the PID algorithm, fuzzy PID algorithm, and fuzzy neural network PID algorithm were employed. The basic principles of each algorithm were first introduced. Then, a mathematical simulation model for beam current control was constructed based on the processing of experimental data from electron beam emission experiments and the theoretical formulas related to electron accelerator beam emission. The three algorithms were subsequently applied to this mathematical simulation model within MATLAB’s Simulink environment. Finally, simulations were conducted in Simulink, with the desired beam current set to 100 mA and the simulation time to 40 seconds. A 5% step response (5 mA) was introduced at 25 seconds as a disturbance. The performance of each algorithm was then compared and analyzed in terms of stabilization time, overshoot, and post-disturbance recovery time. The results show that compared with the PID algorithm, the performance of the fuzzy PID algorithm and the fuzzy neural network PID algorithm is significantly improved. Specifically, the system stabilization time of the fuzzy PID algorithm is reduced by 59.6%, the overshoot is reduced by 48.9%, and the post-disturbance recovery time is reduced by 64.9%. The fuzzy neural network PID algorithm improves these indicators more significantly, the stabilization time is reduced by 77.9%, the overshoot is reduced by 79.6%, and the post-disturbance recovery time is reduced by 87.1%. Based on these results, it is concluded that the fuzzy PID algorithm and the fuzzy neural network PID algorithm can improve the performance of the electron accelerator beam control in terms of accuracy and stability. In summary, the fuzzy PID algorithm and the fuzzy neural network PID algorithm have obvious advantages over the PID algorithm in the electron accelerator beam control, which significantly shortens the response time, reduces the overshoot, and can recover more quickly after being disturbed. It is especially suitable for industrial application scenarios that require high-precision and high-efficiency beam control. Future research can further optimize these algorithms and integrate them into the actual electron accelerator control system to ensure their robustness and stability under different operating conditions.