摘要
为解决因热电偶时间常数大,不能准确反映被测温度瞬态变化的问题,提出基于改进天鹰优化算法(IAO)的热电偶动态补偿方法。通过引入Sobol序列初始化种群与动态反向学习策略对天鹰优化算法(AO)进行改进,对实测热电偶动态响应数据进行补偿实验。实验结果表明,经改进算法补偿后,热电偶时间常数由0.097 s缩短至0.009 s,减少近90.7%。IAO补偿可以有效改善热电偶动态特性,减小动态误差。
In order to solve the problem that the thermocouple time constant is too large to reflect the transient change of measured temperature,a dynamic compensation method of thermocouple based on improved aquila optimizer(IAO)was proposed in this paper.The aquila optimizer(AO)was improved by introducing Sobol sequence initialization population and dynamic reverse learning strategy.The experimental results showed that the time constant of the thermocouple was reduced from 0.097 s to 0.009 s,which was nearly 90.7%.The dynamic compensation system obtained by IAO optimization effectively improved the dynamic characteristics of the thermocouple sensor and reduced the dynamic error of the thermocouple sensor.
作者
贺俊
李新娥
崔春生
何玉洁
HE Jun;LI Xine;CUI Chunsheng;HE Yujie(State key Laboratory of Dynamic Measurement Technology,North University of China,Taiyuan 030051,China;School of Electrical and Control Engineering,North University of China,Taiyuan 030051,China)
出处
《探测与控制学报》
CSCD
北大核心
2024年第3期79-85,共7页
Journal of Detection & Control
关键词
天鹰优化算法
热电偶
动态补偿
aquila optimizer
dynamic compensation
thermocouple sensor
