摘要
本文将BP神经网络算法与PID控制结合,优化了中子发生器控制离子源阳极电流PID调节过程,实现了控制器自适应整定,提升了控制效果。基于离子源阳极电流控制模型改进了PID算法,通过MATLAB/Simulink软件对两种算法进行仿真并对比分析。通过LabVIEW控制并采集离子源阳极电流值,使用改进后的BP神经网络PID算法控制程序进行实验。经实验表明,BP神经网络PID对离子源阳极电流控制能够实现参数的自适应整定,相比传统PID控制器超调量更小、响应速度更快,提高了中子发生器的控制稳定性。
This paper combines BP neural network algorithm with PID control algorithm to solve the problem of manual debugging parameters in neutron ion source anode current PID control,and improves the control performance.Based on the ion source anode current control model,the PID algorithm is improved,and the control process is simulated and studied by MATLAB/Simulink software.The ion source anode current value is controlled and collected by LabVIEW,and the improved BP neural network PID algorithm control program was used to conduct experiments.Experimental results show that BP neural network PID can realize adaptive tuning of parameters for ion source anode current control,which is smaller than the traditional PID controller overshoot,and the response speed is faster,which improves the control stability of the neutron generator.
作者
方宁
董翔
梁参军
郝丽娟
FANG Ning;DONG Xiang;LIANG Canjun;HAO Lijuan(School of Electrical Engineering and Automation Anhui University,Hefei 230601,China;Zhongke Shijin(Anhui)Neutron Technology Co.,Ltd.,Hefei 230601,China)
出处
《核电子学与探测技术》
CAS
北大核心
2024年第1期94-100,共7页
Nuclear Electronics & Detection Technology
基金
安徽省科技重大专项(201903c08020003)“高性能油气勘探中子发生器工程化研发”。
