摘要：AbstractThe requirement of a robust, recursive online parameter estimation for real-time identification of faults in UAVs during autonomous flights is increasing. This paper presents a recursive algorithm for estimation of thrust and power coefficients of a propeller-engine driven fixed wing UAV. Extended Kalman Filter with a high fidelity nonlinear 6-DOF model of the UAV is used to estimate a linear fit for the thrust and power coefficients at the current operating point. Minimal sensor suit normally used in the autonomous flight of the UAV is used as the basis of available measurements. High fidelity nonlinear simulations are used to verify the effectiveness of the algorithm in a simulated flight and results are presented.
关键词：KeywordsPropeller Engine IdentificationReal time parameter estimationKalman Filtering
