Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

Wonkeun Youn, Hanseok Ryu, Dongjin Jang, Changho Lee, Youngmin Park, Dongjin Lee, Matthew B. Rhudy

Research output: Contribution to journalArticlepeer-review

Abstract

This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.

Original languageEnglish (US)
Article number9447232
Pages (from-to)5841-5848
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume6
Issue number3
DOIs
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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