机械与电子2025，Vol.43Issue(9)：40-44,50,6.
基于径向基神经网络的半导体激光器温度自抗扰控制
Temperature Rejection Control of Semiconductor Laser Based on Radial Basis Neural Network
摘要
Abstract
In order to solve the problem that semiconductor lasers are easily affected by the operating temperature during gas detection,resulting in wavelength shift,and then reducing the concentration detec-tion accuracy,an auto disturbance rejection control(ADRC)method based on radial basis function neural network(RBFNN)was proposed.An active disturbance rejection controller combined with RBF neural network was designed,and the gradient descent method was used to adjust the nonlinear parameters in the dilated state observer in real time to optimize the dynamic response and anti-disturbance performance of the temperature control system.The experimental simulation results show that the proposed method can significantly improve the temperature control accuracy,the steady-state error is only 0.004 ℃,the over-shoot is 0.93％,and the adjustment time is 13.8 s,which has better control performance and higher practi-cal value than the traditional PID and ADRC methods.关键词
半导体激光器/温度控制/径向基神经网络/自抗扰控制Key words
semiconductor laser/temperature control/RBFNN/ADRC分类
信息技术与安全科学引用本文复制引用
张会珍,孙琦,王立杰,唐思懿,侯男..基于径向基神经网络的半导体激光器温度自抗扰控制[J].机械与电子,2025,43(9):40-44,50,6.基金项目
海南省科技计划三亚崖州湾科技城联合项目资助(2021JJLH0025) （2021JJLH0025）
