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

Area of Science:

• * Engineering and Applied Physics
• * Sensor Technology and MEMS Devices

Background:

• * MEMS gyroscopes are crucial for navigation and motion sensing but are susceptible to temperature-induced drift.
• * Existing temperature compensation methods often struggle to achieve high accuracy across wide temperature ranges.

Purpose of the Study:

• * To develop and validate a robust temperature compensation model for DMFVMGs.
• * To improve the accuracy and stability of MEMS gyroscopes under thermal variations.

Main Methods:

• * Denoising of gyroscope signals using Grey Wolf Optimization Variational Mode Decomposition (GWO-VMD).
• * Temperature drift prediction employing a hybrid Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) model.
• * Integration of GWO-VMD denoising with TCN-LSTM prediction for comprehensive temperature compensation.

Main Results:

• * Significant reduction in rate random walk from 102.929°/h/√Hz to 17.6903°/h/√Hz (82.81% improvement).
• * Substantial decrease in bias instability from 63.70°/h to 1.38°/h (97.83% improvement).
• * Demonstrated enhanced gyroscope performance and stability across diverse temperature conditions.

Conclusions:

• * The proposed GWO-VMD and TCN-LSTM based temperature compensation model effectively mitigates gyroscope errors.
• * The model offers a superior solution for improving MEMS gyroscope accuracy and reliability in variable thermal environments.