现代电子技术2026，Vol.49Issue(16)：1-6,6.DOI:10.16652/j.issn.1004-373X.2026.16.001
基于模糊神经网络的逆变点焊电源控制优化
Inverter spot welding power supply control optimization based on fuzzy neural network
摘要
Abstract
In allusion to the problems of slow dynamic response and insufficient robustness of traditional PID control in AC inverter spot welding power supplies,a fuzzy adaptive PID control strategy with collaborative optimization of hardware and software is proposed to improve the dynamic performance and anti-interference ability of AC inverter spot welding power and adapt to complex and variable welding conditions.At the hardware level,the main circuit is designed based on Multisim,the 600 V IGBT,50 A/1 000 V rectifier bridge and high-precision hall current sensor(CSM050B)are used to optimize the IGBT drive circuit(including optocoupler TLP521 and driver IR2110PBF)and current sampling system.At the software level,a two-input three-output fuzzy controller is constructed to adjust PID parameters online based on real-time error and error change rate,and Simulink is used to construct the model of the fuzzy control algorithm model,so as to realize secondary optimization of parameters by combing with the fuzzy neural network.The results show that,in comparison with traditional PID control,the system overshoot of the proposed strategy is reduced by 14.5%,the steady-state error is reduced by 25%,and the voltage waveform is symmetrical within the frequency range of 50～250 Hz.In comparison with with traditional power-frequency power supplies and similar inverter power supplies,the IGBT driving voltage is stabilized at 15 V/-7.5 V,the energy conversion efficiency is increased by 12%,and the secondary rectification loss is reduced by 9%.It indicates that collaborative optimization of hardware and software can effectively suppress power grid fluctuations and load disturbances,and significantly enhance the dynamic performance and robustness of the system.关键词
交流逆变点焊电源/模糊神经网络/模糊自适应PID控制/IGBT驱动电路/电流采样系统/能量转换效率Key words
AC inverter spot welding power supply/fuzzy neural network/fuzzy adaptive PID control/IGBT drive circuit/current sampling system/energy conversion efficiency分类
信息技术与安全科学引用本文复制引用
张宇浩,牛园园,左广宇,窦银科,马春燕,付骏宇..基于模糊神经网络的逆变点焊电源控制优化[J].现代电子技术,2026,49(16):1-6,6.基金项目
国家自然科学基金青年基金项目:北极海冰融池跨季节演化特征与热力学过程研究(42306260) （42306260）
企业委任横向科研项目(技术开发类):双机头贴片系统及控制算法研发(RH2400001443) （技术开发类）
2023年太原理工大学大学生创新创业训练计划项目(RC2300004310) （RC2300004310）
山西省水利技术推广与应用项目:抗冰型水库水质全天候自动监测浮标关键技术研发及应用(2025GM22) （2025GM22）
极地生态与气候变化教育部重点实验室开放课题:北极海冰内部力学行为跨季节演化观测方法与影响因素研究(SO02025-04) （SO02025-04）
