首页|基于优先级融合算法的高精度黑体温控研究

基于优先级融合算法的高精度黑体温控研究

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  • 国家科技期刊平台
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为优化红外成像光谱仪探测性能,提出了一种具有用户自定义指标和温控精度达到 1.0 mK 的优先级融合控制算法(Priority fusion algorithm,PFA),该算法将基础PID、模糊PID和自抗扰控制算法与BP神经网络算法相融合,能够实现高性能黑体温控.通过Simulink仿真实验,仿真结果表明,与传统算法相比,PFA算法的超调量从 3.606%下降到 0.101%,响应时间从 64 min下降到 14.4 min,温度控制精度达到 1.0 mK.同时搭建了黑体辐射定标平台,物理实验结果与理论模拟结果基本一致.该模型为高精度温控黑体在空间遥感领域的实际应用奠定理论基础,在温控领域具有重大意义.
Research on High-precision Blackbody Temperature Control Based on Priority Fusion Algorithm
To optimize the detection performance of infrared imaging spectrometers,a priority fusion temperature control algorithm(PFA)with user-defined indicators and a temperature control accuracy of 1.0 mK is proposed.This algorithm combines basic proportional-integral-derivative(PID),fuzzy PID,and self-disturbance rejection control algorithms with the BP neural network algorithm to achieve high-performance blackbody temperature control.Results of Simulink simulation experiments show that compared with traditional algorithms,the overshoot of the PFA algorithm decreases from 3.606%to 0.101%,the response time decreases from 64 min to 14.4 min,and the temperature control accuracy reaches 1.0 mK.Simultaneously,a blackbody radiation calibration platform is built,and the physical experimental results are consistent with the theoretical simulation results.This model lays the theoretical foundation for the practical application of the high-precision temperature controlled blackbody in the field of space remote sensing and has remarkable significance in the field of temperature control.

temperature control systemBP neural networkPFA control algorithmtemperature control accuracyradiation calibration

黄浦江、杨文航、朱首正、赵帮健、金海军、金柯、李春来、刘世界

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中国科学院大学杭州高等研究院,浙江 杭州 310000

中国科学院大学,北京 100049

中国科学院上海技术物理研究所,上海 200083

温控系统 BP神经网络 PFA控制算法 温控精度 辐射定标

浙江省科技厅"尖兵领雁"研发攻关计划浙江省博士后基金择优资助项目(2022)杭高院物理与光电工程学院自立项目

2023C03012ZJ2022116B02006C019001

2024

红外技术
昆明物理研究所 中国兵工学会夜视技术专业委员会

红外技术

CSTPCD北大核心
影响因子：0.914
ISSN：1001-8891
年,卷(期)：2024.46(5)
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