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Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV.

An Guo1, Zhou Zhou2, Xiaoping Zhu3

  • 1School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China. guoanuav@mail.nwpu.edu.cn.

Sensors (Basel, Switzerland)
|October 27, 2019
PubMed
Summary

Three new state estimation algorithms for solar-powered unmanned aerial vehicles (UAVs) were developed using the extended Kalman filter (EKF). These algorithms improve control accuracy and reduce costs for various UAV platforms.

Keywords:
extended Kalman filter (EKF), three-stage seriesfull-state directfull-state indirectlow-cost sensormodel calibrationstate estimation

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Area of Science:

  • Aerospace Engineering
  • Control Systems
  • Robotics

Background:

  • Solar-powered unmanned aerial vehicles (UAVs) require cost-effective and accurate flight control.
  • Existing state estimation algorithms may not meet the demands for precision and cost-efficiency in solar UAV applications.

Purpose of the Study:

  • To develop and compare three novel state estimation algorithms based on the extended Kalman filter (EKF) for solar-powered UAVs.
  • To reduce flight controller costs and enhance control accuracy.
  • To evaluate algorithm performance based on structure, estimation accuracy, and platform requirements.

Main Methods:

  • Implementation of three distinct EKF-based state estimation algorithm structures: three-stage series, full-state direct, and full-state indirect.
  • Comparative analysis of algorithm performance using a small, hand-launched, aileron-less solar-powered UAV.
  • Field testing of the three-stage series algorithm on a full-scaled electric hand-launched UAV.

Main Results:

  • The three-stage estimation algorithm achieved a position accuracy of 6 m, suitable for low-cost, low-precision UAVs.
  • The full-state direct algorithm demonstrated a precision of 3.4 m, ideal for low-cost platforms requiring high trajectory tracking accuracy.
  • The full-state indirect method offered comparable precision to the direct method, with enhanced stability for state switching and parameter estimation, making it suitable for larger platforms.
  • Field tests validated the three-stage algorithm with a position estimation accuracy of 23 m.

Conclusions:

  • The developed EKF-based state estimation algorithms effectively improve control accuracy and reduce costs for solar-powered UAVs.
  • Different algorithm structures are suitable for specific UAV platforms and mission requirements, ranging from small, low-cost drones to larger, high-precision applications.
  • The study confirms the feasibility and practical applicability of these advanced estimation techniques in real-world UAV operations.