Modification of MTEA-Based Temperature Drift Error Compensation Model for MEMS-Gyros

Area of Science:

• Micro-Electro-Mechanical Systems (MEMS)
• Materials Science
• Sensor Technology

Background:

• Conventional temperature drift error (TDE) compensation models for MEMS-gyros struggle to decouple Si-based material temperature dependence due to incomplete temperature correlated quantities (TCQ) data.
• This limitation reduces the environmental adaptability of MEMS-gyros in complex conditions.

Purpose of the Study:

• To modify the TDE compensation model for MEMS-gyros using microstructure thermal effect analysis (MTEA).
• To improve the decoupling of temperature dependence in Si-based materials.
• To enhance the environmental adaptability and performance of MEMS-gyros.

Main Methods:

• Studied Si-based material temperature dependence via microstructure thermal expansion effects to extract TCQ.
• Developed a precise TDE test method by analyzing heat conduction between MEMS-gyros and a thermal chamber.
• Employed a radical basis function artificial neural network (RBF ANN) for modified model parameter identification and performance evaluation.

Main Results:

• The modified TDE compensation model demonstrated improved decoupling of Si-based material temperature dependence.
• MEMS-gyros achieved up to a 10% improvement in bias stability compared to the conventional model.
• Enhanced environmental adaptability of MEMS-gyros was observed, broadening their application scope.

Conclusions:

• The modified TDE compensation model effectively addresses the limitations of conventional approaches.
• Improved temperature dependence decoupling and bias stability enhance MEMS-gyros performance.
• The enhanced MEMS-gyros exhibit greater adaptability for diverse and complex operational environments.