Estimation of turbulence and state based on EKF for a tandem Canard UAV

This paper deals with the state and turbulence estimation of a model describing the longitudinal dynamics of an Unmanned Aerial Vehicle (UAV). Due to both the high nonlinearities of the model and the stochastic nature of disturbances, an Extended Kalman Filter (EKF) is proposed. To allow the estimator to be employed on low cost UAV systems, it is assumed that the aircraft is equipped with a low performance GPS, characterized by a relatively low refresh rate. The designed EKF is able to work efficiently in both turbulent and calm atmosphere. In order to obtain information about the performances of the proposed estimator for control purposes, a control system, consisting of the EKF, a PID-type controller and the longitudinal dynamic model of the UAV, is implemented in Matlab-Simulink environment with the aim of verifying the effects of the estimation errors on the tracking of the reference signals. The obtained results are fully satisfactory.