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Virtual Coriolis-Force-Based Mode-Matching Micromachine-Optimized Tuning Fork Gyroscope without a Quadrature-Nulling

Yixuan Wu1,2, Weizheng Yuan1,2, Yanjun Xue1,2

  • 1School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China.

Micromachines
|September 28, 2023
PubMed
Summary

This study introduces a novel micromachine-optimized tuning fork gyroscope that enhances performance by maximizing scale factor and eliminating the need for a quadrature-nulling loop. This design simplifies complexity and improves bias instability for better gyroscope applications.

Keywords:
MEMS gyroscopematching errormode-matchingvirtual Coriolis force

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Area of Science:

  • Microelectromechanical Systems (MEMS)
  • Inertial Navigation
  • Sensor Technology

Background:

  • Traditional tuning fork gyroscopes often require complex quadrature-nulling loops.
  • These loops can increase structural complexity and occupy valuable pick-up electrode space.
  • Coupling between mode-matching and quadrature-nulling loops can degrade performance.

Purpose of the Study:

  • To propose a VCF-based mode-matching micromachine-optimized tuning fork gyroscope.
  • To maximize the scale factor of the gyroscope.
  • To avoid the use of an additional quadrature-nulling loop, thereby reducing complexity and interference.

Main Methods:

  • A mode-matching, closed-loop system was established without a quadrature-nulling loop.
  • Quantitative analysis of convergence and matching error was performed.
  • Optimal straight beam modeling was used to reduce quadrature coupling.

Main Results:

  • Frequency split was significantly narrowed from 20 Hz to 0.014 Hz.
  • The scale factor was improved by a factor of 20.6.
  • Bias instability (BI) was suppressed by a factor of 3.28.

Conclusions:

  • A mode-matching system without a quadrature suppression loop is feasible.
  • The proposed gyroscope design offers a competitive solution for mode-matching applications.
  • The device demonstrates improved performance metrics, including scale factor and bias instability.