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Aerodynamic Derivatives Identification Using a Non-Conservative Robust Kalman Filter

  • Lee, Han-Sung (School of Electrical Engineering, Seoul National University) ;
  • Ra, Won-Sang (School of Mechanical and Control Engineering, Handong Global University) ;
  • Lee, Jang-Gyu (School of Electrical Engineering, Seoul National University) ;
  • Song, Yong-Kyu (School of Aerospace and Mechanical Engineering, Korea Aerospace University) ;
  • Whang, Ick-Ho (Department of Guidance and Control, Agency for Defense Development)
  • Received : 2011.04.12
  • Accepted : 2011.05.25
  • Published : 2012.01.01

Abstract

A non-conservative robust Kalman filter (NCRKF) is applied to flight data to identify the aerodynamic derivatives of an unmanned autonomous vehicle (UAV). The NCRKF is formulated using UAV lateral motion data and then compared with results from the conventional Kalman filter (KF) and the recursive least square (RLS) method. A superior performance for the NCRKF is demonstrated by simulation and real flight data. The NCRKF is especially effective in large uncertainties in vehicle modeling and in measuring flight data. Thus, it is expected to be useful in missile and aircraft parameter identification.

Acknowledgement

Supported by : Agency for Defense Development

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