基于优先级融合算法的高精度黑体温控研究

黄浦江, 杨文航, 朱首正, 赵帮健, 金海军, 金柯, 李春来, 刘世界

黄浦江, 杨文航, 朱首正, 赵帮健, 金海军, 金柯, 李春来, 刘世界. 基于优先级融合算法的高精度黑体温控研究[J]. 红外技术, 2024, 46(5): 576-583.
引用本文: 黄浦江, 杨文航, 朱首正, 赵帮健, 金海军, 金柯, 李春来, 刘世界. 基于优先级融合算法的高精度黑体温控研究[J]. 红外技术, 2024, 46(5): 576-583.
HUANG Pujiang, YANG Wenhang, ZHU Shouzheng, ZHAO Bangjian, JIN Haijun, JIN Ke, LI Chunlai, LIU Shijie. Research on High-precision Blackbody Temperature Control Based on Priority Fusion Algorithm[J]. Infrared Technology , 2024, 46(5): 576-583.
Citation: HUANG Pujiang, YANG Wenhang, ZHU Shouzheng, ZHAO Bangjian, JIN Haijun, JIN Ke, LI Chunlai, LIU Shijie. Research on High-precision Blackbody Temperature Control Based on Priority Fusion Algorithm[J]. Infrared Technology , 2024, 46(5): 576-583.

基于优先级融合算法的高精度黑体温控研究

基金项目: 

浙江省科学技术厅“尖兵”“领雁”研发攻关计划 2023C03012

2022年浙江省博士后基金择优资助 ZJ2022116

杭高院物理与光电工程学院自立项目 B02006C019001

详细信息
    作者简介:

    黄浦江(1998-),男,湖北荆州人,硕士研究生,主要研究领域为温控算法、黑体辐射面研究,E-mail: 1594284529@qq.com

    通讯作者:

    刘世界(1989-),男,河南商丘人,博士后,主要研究领域为气体探测、红外计量,E-mail: liushijie@ucas.ac.cn

  • 中图分类号: TN219

Research on High-precision Blackbody Temperature Control Based on Priority Fusion Algorithm

  • 摘要:

    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。

    Abstract:

    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.

  • 图  1   自抗扰控制系统的结构框图

    Figure  1.   Structural block diagram of active disturbance rejection control system

    图  2   模糊PID控制系统的结构框图

    Figure  2.   Structural block diagram of fuzzy PID control system

    图  3   基于BP神经网络的PFA控制结构

    Figure  3.   PFA control structure based on BP neural network

    图  4   三层PFA神经网络结构图

    Figure  4.   Three layer PFA neural network structure diagram

    图  5   PFA算法模型与建立流程图

    Figure  5.   PFA algorithm model and establishment flowchart

    图  6   PFA控制算法50℃ Simulink仿真图

    Figure  6.   Simulation diagram of PFA control algorithm at 50℃ using Simulink

    图  7   PFA控制算法—25℃ Simulink仿真图

    Figure  7.   PFA control algorithm -25℃ Simulink simulation diagram

    图  8   温度控制系统的方案设计图

    Figure  8.   Design scheme of temperature control system

    图  9   温度控制系统示意图

    Figure  9.   Schematic diagram of temperature control system

    图  10   升温过程的时间-温度曲线图

    Figure  10.   Time temperature curve of heating process

    图  11   升温过程的时间-温度曲线图

    Figure  11.   Time temperature curve of heating process

    图  12   2.4~4.5 μm谱段辐射定标示意图

    Figure  12.   Schematic diagram of radiation calibration in the 2.4-4.5 μm spectral range

    表  1   PFA算法与传统算法的比较

    Table  1   Comparison between PFA algorithm and traditional algorithm

    Basic PID Fuzzy PID ADRC PFA
    Temp accuracy × ×
    Faster response time ×
    Faster stabilization time ×
    Anti-interference ability × ×
    Lower overshoot × ×
    Application surface × ×
    下载: 导出CSV

    表  2   四种控制算法50℃下仿真效果比较

    Table  2   Comparison of simulation effects of four control algorithms at 50℃

    Algorithm Overshoot Response time/min Stable time/min Temperature control precision/mK
    Basic PID 3.606% 6.4 16 4
    Fuzzy PID 3.036% 6.8 20 2
    ADRC 0.205% 64 64 10
    PFA 0.101% 14.4 16 1
    下载: 导出CSV

    表  3   35℃误差评估表

    Table  3   35℃ error evaluation table

    IAE assess ITAE assess
    PFA Algorithm 0.3902 3.2520
    PID Algorithm 0.6067 4.8748
    下载: 导出CSV

    表  4   100℃误差评估表

    Table  4   100℃ error evaluation table

    IAE assess ITAE assess
    PFA Algorithm 9.7455 193.2658
    ADRC Algorithm 10.9575 230.7170
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-19
  • 修回日期:  2023-12-19
  • 网络出版日期:  2024-05-23
  • 刊出日期:  2024-05-19

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