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A Design Methodology of Digital Control System for MEMS Gyroscope Based on Multi-Objective Parameter Optimization.

Haoyu Gu1, Wei Su1, Baolin Zhao1

  • 1Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621999, China.

Micromachines
|January 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new optimization method using genetic algorithms (GA) and adaptive moment estimation (Adam) to enhance Microelectromechanical systems (MEMS) gyroscope control systems, improving noise immunity and performance.

Keywords:
Adam-LMSD algorithmMEMS gyroscopeMonte Carlo analysisclosed-loop systemgenetic algorithm

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Area of Science:

  • Control Systems Engineering
  • Microelectromechanical Systems (MEMS)
  • Optimization Algorithms

Background:

  • MEMS gyroscopes are crucial for inertial navigation but susceptible to fabrication tolerances and noise.
  • Existing control system designs often struggle with robustness and efficiency.
  • Optimizing closed-loop control systems is essential for improving gyroscope performance.

Purpose of the Study:

  • To present a novel multi-objective parameter optimization method for MEMS gyroscope control systems.
  • To enhance the control system's immunity to fabrication tolerances and external noise.
  • To significantly reduce the design process time compared to traditional methods.

Main Methods:

  • A parameterized model of the closed-loop sense mode was derived.
  • Genetic Algorithm (GA) was employed for loop parameter optimization.
  • Monte Carlo analysis was used for robust optimal solution validation.
  • An Adam-least mean square (LMS) demodulator was developed for noisy signals.
  • The digital control system was implemented using Field Programmable Gate Array (FPGA).

Main Results:

  • The optimized control loop demonstrated significantly improved performance.
  • System bandwidth increased from 23 Hz (open-loop) to 101 Hz (closed-loop).
  • Bias instability decreased from 0.0015°/s to 7.52 × 10-4°/s.
  • Scale factor increased from 17.7 mV/(°/s) to 23 mV/(°/s).
  • Scale factor non-linearity reduced from 0.008452% to 0.006156%.

Conclusions:

  • The proposed GA-Adam optimization method effectively enhances MEMS gyroscope control systems.
  • The optimized system exhibits superior robustness against noise and improved performance metrics.
  • This approach offers a significant reduction in design time and improved gyroscope accuracy.