一个新的温度漂移误差估计模型容量MEMS陀螺仪使用热应力变形分析
Bing Qi1, Jianhua Cheng1, Zili Wang1
1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.
Micromachines
|March 28, 2024
概括
一个新的模型通过使用热应力分析和辐射基函数神经网络 (RBFNN) 准确估计温度漂移误差 (TDE) 来提高容量MEMS旋转器 (CMGs) 的偏差稳定性. 这提高了复杂环境中的性能.
科学领域:
- 在 MEMS 技术方面,MEMS 技术
- 传感器工程 传感器工程
- 材料科学 材料科学 材料科学
背景情况:
- 容量MEMS旋转机 (CMGs) 的常规温度漂移误差 (TDE) 估计模型存在温度相关量 (TCQ) 和参数识别不准确的问题,限制了偏差稳定性.
- 现有的模型很难有效地解开Si基材料的温度依赖性,阻碍CMG在各种环境条件下的性能.
研究的目的:
- 开发基于热应力变形分析的CMG新型TDE估计模型.
- 通过改进TDE估计,提高CMG的偏差稳定性和环境适应性.
- 建立一个精确和高效的TDE测试方法.
主要方法:
- 在热应力下分析CMG结构变形,以确定关键的TCQ,包括环境温度变化 (ΔT, ΔT2, ΔT1/2).
- 开发一种新的TDE估计模型,将这些增强的TCQ纳入其中.
- 辐射基函数神经网络 (RBFNN) 的应用用于准确的参数识别,克服传统反向传播神经网络 (BPNN) 的局限性.
- 热流分析以优化温度控制间隔和周期,用于精确的TDE测试,减少实验时间和成本.
主要成果:
- 新的TDE估计模型,利用适当的TCQ和RBFNN,证明了改进的准确性.
- 使用平均平方偏差 (MSD) 的性能评估证实了该模型的有效性.
- 对比显示,新型模型比传统模型平均提高了15%的CMG偏差稳定性.
结论:
- 新的TDE估计模型显著提高了CMG偏差稳定性和环境适应性.
- 准确的TDE估计有效地解开了Si基材料的温度依赖性.
- 开发的模型和测试方法为CMG表征提供了更精确和更有效的方法,扩大了它们的适用性.
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