Wind gust estimation on a small VTOL UAV

Venkatasubramani S.R. Pappu, Yande Liu, Joseph F. Horn, Jared Cooper

Research output: Contribution to conferencePaperpeer-review

20 Scopus citations


This paper describes a Kalman filter based gust identification technique for estimating wind gusts on a small rotary-wing unmanned aerial vehicle (UAV) during flight. The algorithm is designed to estimate wind gusts using only inertial, position, and control actuation sensors (no air data measurements). The technique is demonstrated using flight tests of an off-the-shelf unmanned quad-rotor UAV. An accurate dynamic model of the aircraft was developed using system identification techniques, and the model is used in a Kalman filter to estimate the external wind disturbances. Testing was conducted in an indoor flying lab using a high speed fan to simulate gusts and a motion tracking system to provide accurate state measurements. The motion capture system was used for both closed loop control of the aircraft and in the gust estimation algorithms. The technique can be used to enhance gust rejection on small UAVs or to survey wind fields around objects to understand turbulent airwakes and to validate physics models of these flow fields.

Original languageEnglish (US)
StatePublished - 2017
Event7th AHS Technical Meeting on VTOL Unmanned Aircraft Systems and Autonomy - Mesa, United States
Duration: Jan 24 2017Jan 26 2017


Other7th AHS Technical Meeting on VTOL Unmanned Aircraft Systems and Autonomy
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering


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