Compensation of Temperature-Induced Errors in Quartz Flexible Accelerometers Using a Polynomial-Based Non-Uniform...

Area of Science:

• Inertial Navigation Systems
• Sensor Technology
• Metrology and Measurement

Background:

• Quartz flexible accelerometers (QFAs) are vital for navigation-grade strapdown inertial navigation systems (SINS).
• Temperature variations critically affect QFA bias and scale factor, degrading SINS navigation accuracy.
• Existing compensation methods may lack efficiency in handling dynamic temperature-induced errors.

Purpose of the Study:

• To develop and validate an advanced temperature error compensation method for QFAs.
• To improve the navigation accuracy of SINS under varying temperature conditions.
• To leverage a non-uniform mutation strategy genetic algorithm (NUMGA) for precise model parameter identification.

Main Methods:

• Analysis of the temperature bias mechanism in QFA output.
• Establishment of a polynomial curve model (PCF) for temperature error.
• Optimization of PCF parameters using a non-uniform mutation strategy genetic algorithm (NUMGA) with data from -20 to 40 °C.

Main Results:

• The NUMGA-PCF model effectively identifies optimal parameters, demonstrating a stable adaptation curve.
• Significant reductions in compensation errors across three axes of the QFA in SINS: 612.24 μg, 60.82 μg, and 875.82 μg compared to traditional GA methods.
• Maintained average values and standard deviations of QFA bias below 0.1 μg within the -20 to 40 °C range.

Conclusions:

• The proposed NUMGA-PCF method offers a robust and accurate solution for temperature error compensation in QFAs.
• This approach enhances the overall navigation accuracy and stability of SINS under thermal stress.
• The method ensures reliable performance by maintaining low bias errors across a wide temperature range.