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EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels.

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This study introduces an online method using an extended Kalman filter to estimate all model parameters for multi-rotor unmanned aerial vehicles (UAVs) from onboard sensor data, enabling practical application.

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Area of Science:

  • Robotics and Control Systems
  • Aerospace Engineering
  • System Identification

Background:

  • Accurate model parameter estimation is crucial for multi-rotor unmanned aerial vehicle (UAV) control and performance.
  • Traditional test-bed based identification methods can be time-consuming and may not reflect real-world flight conditions.
  • Onboard sensors offer a potential source for real-time parameter estimation during operation.

Purpose of the Study:

  • To develop and validate a novel online method for estimating all model parameters of multi-rotor UAVs.
  • To investigate the system's observability for parameter identification using onboard sensor measurements.
  • To demonstrate the practical applicability of the proposed method through simulations and experimental flight data.

Main Methods:

  • Development of dynamic models for three classes of multi-rotor UAVs.
  • Nonlinear observability analysis by augmenting the state vector with parameters to be identified.
  • Implementation of an extended Kalman filter (EKF) for online parameter estimation using onboard sensor data.
  • Validation through extensive computer simulations and real flight log data from a custom quadrotor.

Main Results:

  • The nonlinear observability analysis identified specific measurement sets and conditions for successful parameter estimation.
  • Simulation results confirmed the feasibility of estimating all model parameters in a single online process using the EKF.
  • Experimental validation using flight data demonstrated the practical suitability of the proposed estimation method.

Conclusions:

  • The proposed EKF-based online method effectively estimates all model parameters for multi-rotor UAVs using readily available onboard sensor data.
  • The method overcomes limitations of traditional test-bed identification, offering a practical solution for real-time parameter estimation.
  • This approach enhances the potential for adaptive control and improved performance of UAVs in diverse operational scenarios.