作者：李晓琳, 谢帅, 刘鹏

作者单位：中国飞行试验研究院测试所, 陕西 西安 710089

关键词：机载测试系统;低温特性试验;模糊控制;智能温度控制器

摘要：

军用无人机鉴定试飞任务具有高空、低温、长航时且测试数据量大、种类繁多等特点，使机载测试系统稳定性和可靠性面临重大考验。以军机设备环境试验的相关标准为依据，在机载测试系统常规架构的基础上自主设计低温特性试验，并对设备性能进行检测，数据结果反映某些模块受低温影响严重。为解决以上问题，基于模糊控制策略，该文设计一种适用于无人机机载测试系统的智能温度控制器，以仿真实验对比传统PID和模糊PID的控制效果，以飞行试验验证控制器的工作性能。结果表明，设计的机载测试系统低温特性试验能有效发现设备潜在问题，智能温度控制器能够使机载测试系统在低温环境中持续、稳定工作，满足某无人机鉴定试飞–55 ℃、12 h的测试任务需求，可推广应用于其他飞行器。

Design of intelligent temperature controller for airborne test system
LI Xiaolin, XIE Shuai, LIU Peng
Chinese Flight Test Establishment Testing Institute, Xi'an 710089, China
Abstract: There are many characteristics in evaluation flight test of military UAV, such as high altitude, low temperature, long endurance, large amount and wide variety of test data, which take a major test to the reliability and stability of airborne test system. According to current standards of military aircraft equipments' environmental test, a low temperature characteristic test based on the conventional architecture of airborne test system is designed independently, the performance of equipments is checked, the results show that some modules of the test system are seriously affected by low temperature. To solve the problem, based on fuzzy control strategy, an intelligent temperature controller suitable for UAV airborne test system is designed, the control effect of traditional PID and fuzzy PID are compared with simulation experiment, the working performance of the controller is verified by flight test. The results show that the low temperature characteristic test of airborne test system can effectively discover potential equipment problems, the intelligent temperature controller can ensure the airborne test system work continuously and steadily in low temperature environment, satisfies the –55℃ and 12 h test needs of the UAV's evaluation flight test, and can be extended to other air vehicles.
Keywords: airborne test system;low temperature characteristic test;fuzzy control;intelligent temperature controller
2023, 49(5):151-157  收稿日期: 2021-12-31;收到修改稿日期: 2022-02-18
基金项目: 国防基础科研项目（JCKY2016205B006）；工信部民机专项（MIZ-2016-J-97）
作者简介: 李晓琳(1991-),女,黑龙江哈尔滨市人,工程师,硕士,主要从事飞行试验机载测试技术研究
参考文献
[1] 陶贵生. LabVIEW实现的制冷及低温实验测试系统开发[J]. 中国测试, 2005, 31(6): 20-22+35
[2] 张柱. 低温环境实验装置设计与研究[D]. 天津：天津商业大学, 2015.
[3] 张旭, 张一鸣, 王亮, 等. 无人机电池温控系统设计[J]. 电源技术, 2015, 39(5): 965-967
[4] 郭鹏飞, 吴娟, 郭玉明. 某型飞机液压能源系统温度控制[J]. 机械与电子, 2012, 4: 42-45
[5] 王君. 基于模糊控制策略的温室远程智能控制系统研究[D]. 长春：吉林大学, 2015.
[6] 范津齐. 基于模糊自整定PID算法的电锅炉温度控制[D]. 沈阳：沈阳立功大学, 2013.
[7] 宋冬萍. 智能温度测控系统的研究与设计[D]. 苏州：苏州大学, 2010.
[8] WOLLMANN T, MODLER N, DANNEMANN M, et al. Design and testing of composite compressor blades with focus on the vibration behaviour[J]. Composites Part A:Applied Science and Manufacturing, 2016, 92: 183-189
[9] 李闯勤, 李冬梅. 飞机气候环境实验室分布式测试系统研究[J]. 测控技术, 2018, 5(37): 20-23
[10] CHENG G. Brushless DC motor speed control system based on fuzzy PID controller[M]. Network Computing and Information Security. Springer Berlin Heidelberg, 2012: 287-294.
[11] 余昌源, 电加热锅炉温度控制系统的设计及实现[D]. 呼和浩特：内蒙古大学, 2014.
[12] 易艺, 郝建卫, 于新业, 等. 一种中央空调温控器控制系统的设计[J]. 现代电子技术, 2019, 6(42): 109-113
[13] MASSOUR E M, FRANCESCHI M, MAHER M. Self–tuning method of fuzzy system: an application on greenhouse process[J]. World Academy of Science, Engineering and Technology, 2007, 31: 133-137
[14] 张宝峰, 张耀, 朱均超, 等. 基于模糊PID的高精度温度控制系统[J]. 传感技术学报, 2019, 32(9): 1425-1429
[15] 李新, 陈春俊. 基于模糊PID的高速列车车内压力主被动控制[J]. 中国测试, 2020, 46(1): 105-109
[16] ZHANG X X, LI H X, QI C K. Spatically constrained fuzzy-clustering-based Sensor Placement for Spatiotemporal fuzzy-control system[J]. Fuzzy Systems, IEEE Transactions on, 2010, 18(5): 946-957