摘要
针对压阻式压力传感器的温度补偿问题,提出一种将样条插值与最小二乘拟合相结合的补偿算法,并结合传感器标定实验数据进行仿真试验。结果表明该方法相比曲面拟合方法、BP神经网络和RBF神经网络,补偿最大相对误差和平均补偿时间分别为0.103%和0.135 4 s,不仅能够满足高精度测试要求,而且可减少标定工作量达到提升生产效率的目的。
To address this temperature compensation problem of Piezo-resistive pressure sensor,a combined method of spline interpolation and least square fitting was presented.The simulation results based on a calibration experiment demonstrate the maximum relative error of the proposed compensation method is 0.103% as well as the mean compensation time is about 0.135 4 s.Compared with the results come from surface fitting,BP neural networks and RBF neural networks,in addition to short the calibration process the proposed compensation method can also obtain a more satisfactory compensation precision. The compensation results also indicate that the presented temperature compensation method is able to reach a balance between the time cost and the compensation effectiveness,which lays a foundation to the further reach.
作者
李冀
胡国清
周永宏
邹崇
吴翩卉
LI Ji1,2,HU Guo-qing2,3,ZHOU Yong-hong4,ZOU Chong4,WU Pian-hui3(1.Nanchang Hangkong University,Nanchang 330063,China;2.Department of Mechanical and Electrical Engineering,Xiamen University,Xiamen 361005,China;3.School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China;4.Fujian Wide Plus Precision Instruments Co.Ltd,Fuzhou 350015,Chin)
出处
《仪表技术与传感器》
CSCD
北大核心
2018年第6期1-4,10,共5页
Instrument Technique and Sensor
基金
国家科技重大专项项目(2015ZX03003010)
关键词
硅压阻式压力传感器
温度补偿
样条插值
最小二乘拟合
piezo-resistive pressure sensor
temperature compensation
spline interpolation
least square fitting
