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A UKF-NN Framework for System Identification of Small Unmanned Aerial Vehicles

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5 Author(s)
Kallapur, A. ; Sch. of Aerosp., Univ. of New South Wales, Canberra, ACT ; Samal, M. ; Puttige, V. ; Anavatti, S.
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This paper presents a novel system identification framework for small unmanned aerial vehicles (UAVs) by combining an unscented Kalman filter (UKF) estimator with a neural network (NN) identifier. The method is effective for systems with low-cost, erroneous sensors where the sensor outputs cannot be used directly for system identification and control. The UKF state estimator computes error-compensated attitude and velocities by integrating sensor data from an inertial measurement unit (IMU) and a global positioning system (GPS). The NN identifier approximates the nonlinear dynamics of the UAV from the UKF estimated states, hence identifying the system. As an illustration, the UKF-NN system identification framework is applied to fixed-wing as well as rotary-wing 6-DOF multi-input-multi-output (MIMO) nonlinear UAV models.

Published in:

Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on

Date of Conference:

12-15 Oct. 2008