Rigid-body modeling and nonlinear state estimation of an X-configuration quadrotor UAV based on the AscTec hummingbird platform
Keywords:
Quadrotor UAV, Newton–Euler equations, Nonlinear state-space modeling, Extended Kalman Filter, Unscented Kalman Filter, Jacobian linearizationAbstract
In this paper, the mathematical modeling and state estimation of a nonlinear quadrotor UAV are presented by means of MATLAB/Simulink. A 12-state nonlinear dynamic model of the quadrotor was built up using the Newton–Euler equations. This model consists of the translational dynamics, rotational dynamics, and Euler-angle kinematics. The control inputs of the quadrotor are defined as total thrust, roll moment, pitch moment, and yaw moment. Open-loop simulation was carried out in order to investigate the dynamic characteristics of the UAV. It was observed that the dynamic behavior of the quadrotor system is inherently unstable without any feedback control mechanism. Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) approaches were considered for nonlinear state estimation and sensor fusion from measurements with noise. Results indicate that the UKF provides more accurate state tracking than the EKF, and that sensor fusion improves position and attitude estimation compared with individual, unfused sensor signals.
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