整合多个层次参数以实现微电子机械系统陀螺仪尺度因子的自我补偿
Rui Zhou1, Rang Cui1, Daren An1
1School of Instrument and Electronics, North University of China, Taiyuan 030051, China.
Micromachines
|November 27, 2024
概括
这项研究使用自补偿方法提高了微电机械系统 (MEMS) 陀螺仪的温度稳定性. 部分最小平方回归 (PLSR) 算法整合了关键参数,以显著提高尺度因子的热灵敏度.
科学领域:
- 工程 工程师 工程师 工程师
- 传感器技术 传感器技术
- 数据科学数据科学数据科学
背景情况:
- 尺度因子热灵敏度是微电机系统 (MEMS) 陀螺仪的关键性能指标,对于评估惯性传感器温度稳定性至关重要.
- 改善温度稳定的现有方法往往在准确性和范围上面临限制.
研究的目的:
- 提出和验证一种自我补偿方法,以提高MEMS陀螺仪尺度因子的温度稳定性.
- 通过整合初级和二级相关性参数来开发一个规模因子预测模型.
主要方法:
- 使用部分最小平方回归 (PLSR) 算法来整合规模因子的初级 (共振频率,解调阶段角度) 和二次 (驱动控制电压,方格反电压) 相关值.
- 通过PLSR实现这些相关术语的加权融合,以自我补偿.
- 开发基于已识别的相关性术语的规模因子预测模型.
主要成果:
- 拟议的方法显著提高了尺度因子的温度灵敏度.
- 预测规模因子的最大误差在-40°C至60°C的温度范围内为0.124%.
- 尺度因子的温度灵敏度从6180 ppm/°C降低到9.39 ppm/°C.
结论:
- 基于PLSR的自我补偿方法有效地提高了MEMS陀螺仪尺度因子的温度稳定性.
- 开发的预测模型准确地预测了范围广泛的温度范围内的尺度因子行为.
- 这种方法为提高惯性传感器在不同热条件下的可靠性和性能提供了有希望的解决方案.
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