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Attitude Solving Algorithm and FPGA Implementation of Four-Rotor UAV Based on Improved Mahony Complementary Filter.

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Summary

This study introduces a new attitude-solving algorithm for unmanned aerial vehicles (UAVs) using quaternion-based methods and Allan variance for gyroscope error analysis. The algorithm simplifies hardware while maintaining high accuracy for UAV flight control.

Keywords:
Allan varianceFPGAMahony complementary filteringUAVattitude solving

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Area of Science:

  • Aerospace Engineering
  • Robotics
  • Control Systems

Background:

  • Small unmanned aerial vehicles (UAVs) are increasingly vital in various industries.
  • Accurate attitude estimation is crucial for UAV flight control.
  • Existing algorithms often require complex hardware setups.

Purpose of the Study:

  • To develop a novel, hardware-efficient attitude-solving algorithm for UAVs.
  • To improve upon traditional Mahony complementary filtering using quaternion representation and Allan variance.
  • To validate the algorithm's performance on a practical quadrotor UAV platform.

Main Methods:

  • Utilized quaternions to represent attitude matrices.
  • Employed Allan variance for gyroscope error analysis and quantification.
  • Integrated six-axis sensor data (gyroscope, accelerometer) with FPGA processing.
  • Implemented the algorithm on a MPU6050 sensor and FPGA.

Main Results:

  • Six-axis sensor data demonstrated strong agreement with nine-axis data, reducing hardware complexity.
  • FPGA processing of sensor data validated the proposed attitude solution method.
  • The algorithm achieved a Root Mean Square Error (RMSE) below 2° compared to the extended Kalman filter.
  • The system simplified hardware while preserving accuracy and speed.

Conclusions:

  • The proposed algorithm offers a simplified yet accurate and fast solution for UAV attitude estimation.
  • This method has significant potential for application in UAV flight control systems.
  • The integration of quaternion representation and Allan variance analysis proves effective for enhancing attitude-solving algorithms.