对双轴MEMS加速器使用自适应模式分解和混合卷积-循环时间网络建模的温度补偿的实验研究
Yanchao Ren1, Guodong Duan2, Jingjing Jiao2
1College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China.
Micromachines
|November 27, 2025
概括
本研究引入了一种使用自适应模式分解 (AMD),灰狼优化 (GWO) 和混合卷积-循环时间网络 (HCR-TN) 的新方法,以减少微电机系统 (MEMS) 加速度计中的温度误差.
科学领域:
- 传感器技术 传感器技术
- 仪器化 仪器化 仪器化
- 信号处理 信号处理
背景情况:
- 微电机系统 (MEMS) 加速度计对于运动传感至关重要.
- 温度变化导致显著的偏差偏移,降低了加速度计的准确性.
- 现有的补偿方法经常与复杂的温度依赖性作斗争.
研究的目的:
- 为双轴MEMS加速度计开发一种新的温度补偿方法.
- 为了减轻温度引起的偏差偏移,并提高传感器性能.
- 提高MEMS加速度计在各种环境中的可靠性和准确性.
主要方法:
- 整合适应模式分解 (AMD) 用于信号分析.
- 灰狼优化 (GWO) 的应用用于参数调整.
- 使用混合卷积-循环时间网络 (HCR-TN) 进行补偿建模.
- 在广泛的温度范围内 (-40°C至+60°C) 进行实验验证.
主要成果:
- 在偏差稳定性方面取得了显著的改进.
- 拟议的补偿方法有效地减少了两个轴上的温度诱导漂移.
- 该算法在实践测试中证明了对噪音和机械干扰的稳定性.
结论:
- 新的AMD-GWO-HCR-TN方法为MEMS加速器温度补偿提供了一个强大的解决方案.
- 这种方法提高了传感器的准确性和可靠性,用于现实世界的应用.
- 这些发现为更可靠的惯性传感系统铺平了道路.
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