Research on High-precision Blackbody Temperature Control Based on Priority Fusion Algorithm
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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.
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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
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图 6 PFA控制算法50℃ Simulink仿真图
Figure 6. Simulation diagram of PFA control algorithm at 50℃ using Simulink
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图 7 PFA控制算法—25℃ Simulink仿真图
Figure 7. PFA control algorithm -25℃ Simulink simulation diagram
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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 √ × × √ 
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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
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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
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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
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