霍尔效应式力传感器的温度补偿
Temperature compensation of Hall-effect force sensor
江银玉 1丁勇 2左锋 1卢文科1
作者信息
- 1. 东华大学信息科学与技术学院 上海 201620
- 2. 上海宇航系统工程研究所 上海 201108
- 折叠
摘要
针对霍尔效应式力传感器温度漂移的问题,提出了混沌自适应鲸鱼优化BP神经网络(CIWOA-BP)的温度补偿新模型.该模型通过Cubic映射作为初始鲸鱼种群生成方法,以提高种群的质量和分布均匀性.引入自适应权重调整鲸鱼的收缩包围机制,提高算法的全局搜索能力和收敛性.利用CIWOA算法对反向传播(back propagation,BP)神经网络的初始权值和阈值进行优化,使模型具有更好的测量精度和稳定性.研究结果表明,温度补偿以后霍尔效应式力传感器的灵敏度温度系数αs由5.08×10-3/℃减少至9.8×10-5/℃,减小了 2个数量级,温度附加相对误差由补偿前的19.82％减小到了 0.38％,减小了 52倍以上,从而有效的减弱了温度对测量结果的影响.
Abstract
Aiming at the problem of temperature drift of Hall effect force sensor,a new temperature compensation model of chaotic adaptive whale optimized BP neural network(CIWOA-BP)was proposed.This model uses Cubic mapping as the initial whale population generation method to improve the quality and distribution uniformity of the population.The adaptive weight was introduced to adjust the shrinking and bounding mechanism of the whale to improve the global search ability and convergence of the algorithm.The CIWOA algorithm is used to optimize the initial weights and thresholds of the back propagation(BP)neural network,so that the model has better measurement accuracy and stability.Research results indicate that after temperature compensation,the temperature coefficient of sensitivity for the Hall effect force sensor decreases from 5.08× 10-3/℃ to 9.8× 10-5/℃,reducing by two order of magnitude.The temperature-induced relative error decreases from 19.82％before compensation to 0.38％,which is reduced by over 52 times,effectively mitigating the influence of temperature on measurement results.
关键词
混沌自适应鲸鱼优化算法/BP神经网络/霍尔效应式力传感器/温度补偿Key words
chaotic adaptive whale optimization algorithm/BP neural network/Hall effect force sensor/temperature compensation引用本文复制引用
基金项目
国家自然科学基金(61274078)
中国纺织工业联合会"纺织之光"应用基础研究项目(J201608)
出版年
2024
NETL
NSTL
万方数据