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A Novel Temperature Drift Error Estimation Model for Capacitive MEMS Gyros Using Thermal Stress Deformation Analysis
Bing Qi1, Jianhua Cheng1, Zili Wang1
1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.
Micromachines
|March 28, 2024
Summary
A new model improves bias stability in Capacitive MEMS Gyros (CMGs) by accurately estimating temperature drift error (TDE) using thermal stress analysis and a Radial Basis Function Neural Network (RBFNN). This enhances performance in complex environments.
Area of Science:
- MEMS Technology
- Sensor Engineering
- Materials Science
Background:
- Conventional Temperature Drift Error (TDE) estimation models for Capacitive MEMS Gyros (CMGs) suffer from inadequate Temperature Correlated Quantities (TCQs) and inaccurate parameter identification, limiting bias stability.
- Existing models struggle to effectively decouple the temperature dependence of Si-based materials, hindering CMG performance in varied environmental conditions.
Purpose of the Study:
- To develop a novel TDE estimation model for CMGs based on thermal stress deformation analysis.
- To enhance the bias stability and environmental adaptability of CMGs through improved TDE estimation.
- To establish a precise and efficient TDE testing methodology.
Main Methods:
- Analysis of CMG structural deformation under thermal stress to identify key TCQs, including ambient temperature variations (ΔT, ΔT², ΔT¹/²).
- Development of a novel TDE estimation model incorporating these enhanced TCQs.
- Application of a Radial Basis Function Neural Network (RBFNN) for accurate parameter identification, overcoming limitations of conventional Back-Propagation Neural Networks (BPNNs).
- Heat flux analysis to optimize temperature control intervals and periods for precise TDE testing, reducing experimental time and cost.
Main Results:
- The novel TDE estimation model, utilizing adequate TCQs and RBFNN, demonstrated improved accuracy.
- Performance evaluation using Mean Square Deviation (MSD) confirmed the model's effectiveness.
- Comparison showed the novel model improved CMG bias stability by an average of 15% over the conventional model.
Conclusions:
- The novel TDE estimation model significantly enhances CMG bias stability and environmental adaptability.
- Accurate TDE estimation effectively decouples the temperature dependence of Si-based materials.
- The developed model and testing method offer a more precise and efficient approach for CMG characterization, broadening their applicability.
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