Precise temperature control method for spacecraft under low measurement accuracy constraints
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摘要:
针对低测温分辨力限制下的航天器精密控温需求，提出基于卡尔曼滤波(KF)的高精度控温方法。该方法构建控温对象的热模型，通过卡尔曼滤波方法预估温度状态变化、抑制温度测量噪声，基于滤波数据进行闭环控制实现高精度控温。针对该方法参数整定困难的问题，提出依据滑动均值滤波结果进行持续修正的优化卡尔曼滤波(mKF)方法。通过试验和仿真，分析了参数取值对控温精度的影响规律。结果表明：在测温分辨力低于100 mK的限制条件下，控温系统在应用所提卡尔曼滤波方法后可提升控温精度并增强对大PI参数的适应性，在较宽的参数范围内取得优于10 mK的稳定度。应用所提优化卡尔曼滤波方法后，系统在大PI控制参数下的控温精度进一步提升，且对PI控制参数、滤波参数、热模型参数的敏感度更低，具有更小的参数整定难度和更强的鲁棒性。
Abstract:To meet the demand for precise temperature control in spacecraft under the constraint of low temperature measurement resolution, this paper proposes a high-precision temperature control method based on Kalman filtering (KF). This method first constructs a thermal model of the controlled object. The Kalman filter algorithm is then utilized to estimate temperature state changes and suppress temperature measurement noise. Finally, closed-loop control based on the filtered data is implemented to achieve high-precision temperature regulation. A modified Kalman filter (mKF) approach that incorporates continuous correction based on moving average filter results is suggested in order to address the problem of parameter adjustment inherent in this method. Through experiments and simulations, the influence of parameter settings on temperature control accuracy was analyzed. The findings demonstrate that the temperature control system using Kalman filtering improves control accuracy and adaptability to larger PI parameter values under the constraint of a temperature measurement resolution lower than 100 mK, achieving a stability better than 10 mK across a wider parameter range. After applying the modified Kalman Filtermethod, the system's temperature control accuracy under large PI control parameters is further improved. Moreover, the system exhibits lower sensitivity to PI control parameters, filtering parameters, and thermal model parameters, resulting in reduced parameter tuning difficulty and enhanced robustness.
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Key words:
- temperature resolution /
- temperature control /
- Kalman filtering /
- parameter tuning /
- robustness
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图 3 各类温度传感器的测温数据
Figure 3. Temperature measurement data from various types of temperature sensors
图 4 小PI参数组试验的控温精度 ($ {K}_{{\mathrm{p}}} $=0.57, $ {K}_{{\mathrm{i}}} $=0.002 2)
Figure 4. Temperature control accuracy in experiment under small PI parameters ($ {K}_{{\mathrm{p}}} $=0.57, $ {K}_{{\mathrm{i}}} $=0.002 2)
图 5 大PI参数组试验的控温精度 ($ {K}_{{\mathrm{p}}} $=57, $ {K}_{{\mathrm{i}}} $=0.22)
Figure 5. Temperature control accuracy in experiment under large PI parameters ($ {K}_{{\mathrm{p}}} $=57, $ {K}_{{\mathrm{i}}} $=0.22)
图 7 稳态阶段测温数据的幅值谱密度
Figure 7. Amplitude spectrum density of temperature measurement data in steady state
图 9 仿真结果和试验测量值的对比
Figure 9. Comparison between simulation results and experimental measurements
图 10 不同PI参数取值下的控温精度
Figure 10. Temperature control accuracy under different PI parameter
图 11 小PI参数条件下仿真的控温精度($ {K}_{{\mathrm{p}}} $=0.57,$ {K}_{{\mathrm{i}}} $=0.002 2)
Figure 11. Temperature control accuracy in simulation under small PI parameters ($ {K}_{{\mathrm{p}}} $=0.57,$ {K}_{{\mathrm{i}}} $=0.002 2)
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[1] 冯建朝, 张晓峰, 梁鸿, 等. 太极二号卫星精密热控关键技术及试验验证[J]. 宇航学报, 2023, 44(1): 132-142.Feng J C, Zhang X F, Liang H, et al. Key technology and experimental verification of precision thermal control of Taiji-2 satellite[J]. Journal of Astronautics, 2023, 44(1): 132-142(in Chinese). [2] Luo J, Chen L S, Duan H Z, et al. TianQin: a space-borne gravitational wave detector[J]. Classical and Quantum Gravity, 2016, 33(3): 035010. [3] Brooks T E, Stahl H P. Precision thermal control technology to enable thermally stable telescopes[J]. Journal of Astronomical Telescopes, Instruments, and Systems, 2022, 8(2): 024001. [4] Torresi P. MICROSCOPE thermal control design and first in-orbit thermal control performance results[C]//Proceedings of the 47th International Conference on Environmental Systems. Reston: AIAA, 2017. [5] 童叶龙, 李国强, 耿利寅. 航天器精密控温技术研究现状[J]. 航天返回与遥感, 2016, 37(2): 1-8.Tong Y L, Li G Q, Geng L Y. A review on precise temperature control technology for spacecraft[J]. Spacecraft Recovery & Remote Sensing, 2016, 37(2): 1-8(in Chinese). [6] 韩潇, 周盈, 黄海, 等. 高精度动态温度控制系统设计与验证[J]. 北京航空航天大学学报, 2025, 51(5): 1539-1547.Han X, Zhou Y, Huang H, et al. Design and verification of high-precision dynamic temperature control system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2025, 51(5): 1539-1547(in Chinese). [7] Jiang L J, Liu C D, Zhu L X, et al. High-precision and wide-range temperature measurement and control system of satellite-borne calibration blackbody[J]. Measurement, 2024, 231: 114591. [8] 刘红, 张晓峰, 冯建朝, 等. 精密热控技术在太极一号卫星上的应用[J]. 空间科学学报, 2021, 41(2): 337-341.Liu H, Zhang X F, Feng J C, et al. Application of precision thermal control techniques in Taiji-1 satellite[J]. Chinese Journal of Space Science, 2021, 41(2): 337-341(in Chinese). [9] Zhang X F, Liang H, Tan H P, et al. Temperature stability of the Taiji-1 satellite in operational orbit[J]. International Journal of Modern Physics A, 2021, 36(11-12): 2140022. [10] Wudy F E, Moosbauer D J, Multerer M, et al. Fast micro-kelvin resolution thermometer based on NTC thermistors[J]. Journal of Chemical & Engineering Data, 2011, 56(12): 4823-4828. [11] Zhang B, Zhu X Y, Zhang X F, et al. A temperature measurement system with high resolution and low noise[J]. International Journal of Modern Physics A, 2021, 36(11-12): 2140024. [12] Armano M, Audley H, Baird J, et al. Temperature stability in the sub-milliHertz band with LISA Pathfinder[J]. Monthly Notices of the Royal Astronomical Society, 2019, 486(3): 3368-3379. [13] Eleffendi M A, Johnson C M. Application of Kalman filter to estimate junction temperature in IGBT power modules[J]. IEEE Transactions on Power Electronics, 2016, 31(2): 1576-1587. [14] 李保强. 基于模糊PID的叶腊石烤箱温度控制系统研究[D]. 郑州: 郑州大学, 2010.Li B Q. Research on temperature control system of pyrophyllite oven based on fuzzy PID[D]. Zhengzhou: Zhengzhou University, 2010(in Chinese). [15] Dong H, Li X P, He X, et al. A two-degree-of-freedom controller for a high-precision air temperature control system with multiple disturbances[J]. Case Studies in Thermal Engineering, 2023, 50: 103442. [16] Lee D J, Alfriend K. Adaptive sigma point filtering for state and parameter estimation[C]//Proceedings of the AIAA/AAS Astrodynamics Specialist Conference and Exhibit. Reston: AIAA, 2004. [17] Sakov P, Haussaire J M, Bocquet M. An iterative ensemble Kalman filter in the presence of additive model error[J]. Quarterly Journal of the Royal Meteorological Society, 2018, 144(713): 1297-1309. [18] Zorzi M. Robust Kalman filtering under model perturbations[J]. IEEE Transactions on Automatic Control, 2017, 62(6): 2902-2907. [19] Galanis G, Anadranistakis M. A one-dimensional Kalman filter for the correction of near surface temperature forecasts[J]. Meteorological Applications, 2002, 9(4): 437-441. [20] Tang X S, Hu B, Zang H H, et al. Bootstrap method for characterizing statistical uncertainty in bivariate shear strength parameters and its application to reliability-based design of slopes[J]. Geological Journal, 2024, 59(9): 2416-2425. -
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