Parameter identification methods for processing flight data are frequently used to validate and improve a preflight aerodynamic database and, specifically, to reduce the associated uncertainties. In this framework, the paper describes an identification methodology developed for the first flying test bed of the Italian Aerospace Research Center, a demonstrator of technologies relevant to future reusable launch vehicles. The analysis is focused on aerodynamic modeling of the reentry vehicle configuration in the transonic flow regime, in which flight control system performance is affected by a significant level of parameter uncertainty. The parameter estimation is formulated as a nonlinear filtering problem and solved through a multistep approach, in which the aerodynamic coefficients are identified first and, in a following phase, a set of model parameters is updated. In each step, an unscented Kalman filter is used as a recursive estimation algorithm. The methodology is applied to the flight data of the Dropped Transonic Flight Test mission of the vehicle, carried out during the winter of 2007. The reported results demonstrate the good characteristics of the technique in terms of convergence, reduction of uncertainty of the a priori aerodynamic model, and capability of extracting the information content from a rather limited set of flight data on vehicle response

Vehicle preflight aerodynamic databases are frequently validated and improved using system identification techniques that require the availability of flight data and the definition of suitable aerodynamic models. In this framework, the paper presents a novel theoretical aerodynamic model to be used for the aerodynamic identification of a lifting body in subsonic, transonic, and supersonic regimes. The proposed model is based on the general properties of the continuity equation in the von Kármán form. Each aerodynamic coefficient incorporates free parameters to be estimated from the flight data analysis and takes into account the simultaneous effects of the several vehicle state variables. The proposed model was applied to the identification from the flight data of an experimental vehicle of the Italian Aerospace Research Centre. Identification results show that the model is able to perform an excellent fitting of the aerodynamic coefficients experimented in flight by the vehicle

This paper describes the guidance, navigation and control challenges posed by the Unmanned Space Vehicles Program. Within the framework of this program the Italian Aerospace Research Center has conceived several advanced GN&C technologies useful in the Terminal Area Energy Management phase of a re-entry flight pattern. These technologies were flight tested during the first two dropped transonic flight tests (DTFT1 and DTFT2) of the program. More specifically, this paper will present the design of the adaptive guidance algorithms developed to accomplish the mission objectives of the DTFT2 flight test. Flight results will be shown in order to state the performance of the guidance strategy putting in evidence, where possible, its most promising aspects for future TAEM applications