神经网络PID算法在漆包线检测仪中的应用  

Application of Neural Network PID Algorithm in the Enameled Wire Detection Instrument

作　　者：雷翔霄[1,2] 徐立娟[1] LEI Xiangxiao;XU Lijuan(School of Electronic Information Engineering,Changsha Social Work College,Changsha Hunan 410004,China;College of Electrical and Information Engineering,Hunan University,Changsha Hunan 410082,China)

机构地区：[1]长沙民政职业技术学院电子信息工程学院,湖南长沙410004 [2]湖南大学电气与信息工程学院,湖南长沙410082

出　　处：《机床与液压》2020年第19期104-107,共4页Machine Tool & Hydraulics

基　　金：湖南省自然科学基金项目(2020JJ7088);国家自然科学基金面上项目(51677063)。

摘　　要：为检测漆包线的热性能,设计了温度控制系统。利用BP神经网络在线修正PID参数,从而获取最优的一组参数,并将BP-PID控制算法应用于该温控系统。利用MATLAB进行仿真试验,验证了该控制系统能达到较好的控制效果。利用漆包线检测仪进行实物试验,结果表明:当控制温度为120℃时,该控制方式与传统PID控制相比具有更好的控温效果,超调率小于4%、稳态精度小于2℃,达到了预期目的。In order to detect the thermal performance of the enameled wire, a temperature control system was designed. BP neural network was used to correct the PID parameters online to obtain the optimal parameters, and BP-PID control algorithm was applied to the temperature control system.The simulation experiment was carried out by using MATLAB, and the experimental results verified that the control system could achieve better control effect. Enamelled wire detectors were used for the physical experiment. The results show that when the control temperature is 120 ℃, compared with the traditional PID control, the control mode has better temperature control effect, the overshoot rate is less than 4%, the steady-state precision is less than ±2 ℃, reaching the expected goal.

关 键 词：漆包线 热性能 BP神经网络 PID控制算法 检测仪 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]