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Angular Rate Sensing with GyroWheel Using Genetic Algorithm Optimized Neural Networks.

Yuyu Zhao1, Hui Zhao2, Xin Huo3

  • 1Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, China. 14B904037@hit.edu.cn.

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Summary
This summary is machine-generated.

This study presents a novel method for accurately measuring angular rates using the GyroWheel, a device for small spacecraft attitude control. The technique employs genetic algorithm-optimized neural networks for precise rate sensing across the entire operating range.

Keywords:
GyroWheelangular rate sensingartificial neural networkgenetic algorithmlarge tilt angles

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Area of Science:

  • Aerospace Engineering
  • Control Systems
  • Spacecraft Dynamics

Background:

  • The GyroWheel is a critical component for small spacecraft attitude control, providing three-axis torques and two-axis angular rate sensing.
  • Complex dynamics arising from large rotor tilt angles and de-tuned spin rates pose significant challenges for accurate angular rate measurement.

Purpose of the Study:

  • To investigate and develop an effective method for angular rate sensing using the GyroWheel, addressing the limitations of existing techniques.
  • To provide a practical approach for estimating angular rates across the entire operating range of the GyroWheel.

Main Methods:

  • A simplified rate sensing equation was introduced and its error characteristics analyzed.
  • A novel rate sensing principle based on torque balance theory was developed.
  • Explicit genetic algorithm-optimized neural networks were employed to estimate angular rates using measurable GyroWheel parameters.

Main Results:

  • The developed method effectively estimates angular rates by utilizing measurable values such as tilt angles, spin rate, and torque coil currents.
  • Neural network weights and biases were optimized using a genetic algorithm to enhance sensing accuracy.
  • Simulation results demonstrated the effectiveness and accuracy of the proposed angular rate sensing method.

Conclusions:

  • The proposed method offers a robust solution for angular rate sensing in GyroWheels, overcoming the challenges posed by complex dynamics.
  • This advancement is crucial for improving the attitude control and navigation capabilities of small spacecraft.
  • The use of genetic algorithm-optimized neural networks provides a practical and effective approach for real-time angular rate estimation.