基于GWO-VMD-TCN-LSTM组合算法的多帧振动微机电系统陀螺仪温度补偿

Multi-Frame Vibration MEMS Gyroscope Temperature Compensation Based on Combined GWO-VMD-TCN-LSTM Algorithm.

作者信息

Li Ao, Cui Ke, An Daren, Wang Xiaoyi, Cao Huiliang

机构信息

School of Engineer, The Hong Kong University of Science and Technology, Hong Kong, China.

School of Instrument and Electronics, North University of China, Taiyuan 030051, China.

出版信息

Micromachines (Basel). 2024 Nov 15;15(11):1379. doi: 10.3390/mi15111379.

DOI:10.3390/mi15111379

PMID:39597191

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11596031/

Abstract

This paper presents a temperature compensation model for the Multi-Frame Vibration MEMS Gyroscope (DMFVMG) based on Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD) for denoising and a combination of the Temporal Convolutional Network (TCN) and the Long Short-Term Memory (LSTM) network for temperature drift prediction. Initially, the gyroscope output signal was denoised using GWO-VMD, retaining the useful signal components and eliminating noise. Subsequently, the denoised signal was utilized to predict temperature drift using the TCN-LSTM model. The experimental results demonstrate that the compensation model significantly enhanced the gyroscope's performance across various temperatures, reducing the rate random wander from 102.929°/h/√Hz to 17.6903°/h/√Hz and the bias instability from 63.70°/h to 1.38°/h, with reductions of 82.81% and 97.83%, respectively. This study validates the effectiveness and superiority of the proposed temperature compensation model.

摘要

本文提出了一种基于灰狼优化变分模态分解（GWO-VMD）去噪的多帧振动微机电系统陀螺仪（DMFVMG）温度补偿模型，并结合时间卷积网络（TCN）和长短期记忆（LSTM）网络进行温度漂移预测。首先，利用GWO-VMD对陀螺仪输出信号进行去噪，保留有用信号成分并消除噪声。随后，利用去噪后的信号通过TCN-LSTM模型预测温度漂移。实验结果表明，该补偿模型显著提高了陀螺仪在不同温度下的性能，将随机游走率从102.929°/h/√Hz降低到17.6903°/h/√Hz，偏置不稳定性从63.70°/h降低到1.38°/h，降幅分别为82.81%和97.83%。本研究验证了所提出的温度补偿模型的有效性和优越性。