Identification of the nonlinear dynamic model of sailplanes involving state estimation and image processing for actuator signal computation

The primary scope of the paper lies on the identification of an aircraft's nonlinear dynamic model. It is assumed that the aircraft has no inbuilt navigational system, nor any sensors mounted on its control surfaces. The flight of the airplane is influenced by the control column and pedals manipulated by the pilot whose positions can only visually be observed. This situation can often occur in the first phase of control system development of airplanes. Hence, for the time of data logging, an external sensory system (GPS, IMU) and a camera system were deployed on the airplane supporting the collection of flight data for state estimation and model identification. An earlier paper discussed the computation of the actuator signals thus the paper deals mainly with the state estimation and model identification. State estimation is based on two-level Extended Kalman Filters with additional correction in an external loop. System identification is based on the dynamical equations of rigid body with additional weighted nonlinear terms for 3D forces and torques. Wind effects are taken into consideration. From the inertial parameters only the mass is known. Dominating nonlinear functions in the force and torque model are selected by using hypotheses tests. The results are presented for a real sailplane using flight data.