基于微形变分析的电容式MEMS加速度计温漂误差精密估计方法
Precise temperature drift error estimation method for capacitive MEMS accelerometers based on micro-deformation analysis
齐兵 1程建华 1赵砚驰 1汪籽粒1
作者信息
- 1. 哈尔滨工程大学智能科学与工程学院,黑龙江哈尔滨 150001
- 折叠
摘要
针对传统的电容式微机电系统(micro-electro-mechanical system,MEMS)加速度计(capacitive MEMS accelerometers,CMA)温漂误差(temperature drift error.TDE)补偿方法存在非精准溯源TDE致使TDE估计精度低、反复尝试估计模型构型导致构建过程复杂繁琐的问题,提出基于微形变分析的CMA TDE精密补偿方法.首先,通过微形变分析内部结构精准溯源TDE,基于径向基函数神经网络(radial basis function neural network,RBFNN)构建改进型TDE精密估计模型;其次,基于专家经验和模糊理论提出Expert-Fuzzy辅助决策下TDE估计模型辨识方法,为改进模型提供有效的构型指导;然后,设计升温试验测试CMA,构建传统模型和改进模型并通过对比其输出偏置稳定性评估TDE估计性能.实验结果表明,改进模型构建过程大大简化,补偿后CMA偏置稳定性提升约35％.本方法能够更加精准地估计TDE,有效解耦硅基材料的温度依赖性并提升CMA的环境适应性.
Abstract
Owing that the conventional temperature drift error(TDE)estimation method for capacitive micro-electro-mechanical system(MEMS)accelerometers(CMA)has demerits of inaccurate TDE traceability and tedious model establishing process,low accuracy and high complexity are introduced inevitably.A precise TDE estimation method of CMA based on micro-deformation analysis is proposed.Firstly,TDE is traced accurately with micro-deformation analysis,and a modified TDE estimation model is established with radial basis function neural network(RBFNN).Secondly,an accurate model identification method is proposed under Expert-Fuzzy assisted decision-making based on expert experience and fuzzy theory,which offers effective structural configuration guidance to the modified model.Then,temperature experiment is designed to test CMA,and the conventional and modified models are evaluated in performance by comparing their bias stability.Experimental results show that the establishing process for the modified model is greatly simplified,and its bias stability is improved by 35％.It ensures that TDE of CMA can be estimated much more precisely,which decouples temperature dependency of Si-based material and improves the environmental adaptability of CMA.
关键词
微机电系统/温漂误差估计/微形变分析/Expert-Fuzzy辅助决策/径向基函数神经网络Key words
micro-electro-mechanical system(MEMS)/temperature drift error(TDE)estimation/micro-deformation analysis/Expert-Fuzzy aided decision-making/radial basis function neural network(RBFNN)引用本文复制引用
基金项目
国家自然科学基金(62003108)
国家自然科学基金(62073093)
国家自然科学基金(61773132)
国家自然科学基金(61803115)
国家自然科学基金(62003109)
出版年
2024
NETL
NSTL
万方数据