Federico Corraro

Path Generation and Tracking in 3-D for UAVs

In this brief, we consider the problem of 3-D path generation and tracking for unmanned air vehicles (UAVs). The proposed path generation algorithm allows us to find a path satisfying arbitrary initial and final conditions, specified in terms of position and velocity. Our method assumes that aircraft structural and dynamic limitations can be translated in a turn radius constraint; therefore, the generated paths satisfy a constraint on the minimum admissible turning radius. The proposed algorithm for the path tracking guarantees, under specified assumptions, that the tracking error, both in position and in attitude, asymptotically tends to zero. The work has been carried out with reference to the UAV of the Italian Aerospace Research Center (CIRA). Simulation results for both the path generation and the tracking algorithms are presented; the latter have been obtained using a detailed 6-degree-of-freedom model of the CIRA UAV in the presence of wind and turbulence.

On-line guidance with trajectory constraints for terminal area energy management of re-entry vehicles

In this article, an on-line guidance strategy for terminal area energy management phase of a re-entry flight is proposed. In order to fulfil the objectives of this flight phase, the algorithm continuously performs long- and short-term on-line trajectory generation, also accounting for the most relevant vehicle and trajectory constraints. Long-term guidance computes a reference trajectory which minimizes the distance from the final target. Short-term guidance generates the control commands by computing a trajectory that minimizes the displacement from the reference one, thus compensating for the errors due to the environmental disturbances and to the uncertainties of the vehicle model. In the proposed guidance strategy, the main vehicle performance constraints are appropriately accounted for, thus guaranteeing adaptivity in the failure situations where the manoeuvring capabilities are reduced. The proposed guidance strategy has been developed in the framework of the unmanned space vehicle program of the Italian Aerospace Research Centre for the execution of Dropped Transonic Flight Test second mission. In this article, the algorithm performances and robustness to the vehicle initial state have been assessed through a preliminary Monte Carlo analysis. Furthermore, several simulations with bank angle and angle of attack limitations have shown the effectiveness of the algorithm in the presence of reduced manoeuvring capabilities.

A novel 3D analytical algorithm for autonomous collision avoidance considering cylindrical safety bubble

This paper presents an innovative 3D analytical algorithm for the resolution of the pair-wise non-cooperative collision avoidance problem between aircrafts. The proposed algorithm addresses the above described problem by using an innovative approach, based on the consideration of a cylindrical safety bubble, and it is able to obtain an optimal three-dimensional analytical solution for this problem. This novel approach allows different minimum separations on the vertical and horizontal planes with respect to the nominal trajectory to be achieved, so minimizing the impact of the collision avoidance maneuver on surrounding traffic. Moreover, the algorithm has the very interesting feature that it does not require the solution of any non deterministic and/or iterative problem, resulting suitable for real-time applications. This is due to the capability of the algorithm to find a closed form solution for the kinematic optimization problem here considered. The solution of the collision avoidance problem requires the simultaneous change of all control variables (speed module, track and slope angles), aiming to assure the required safety level and, at the same time, to minimize aircraft deviation from the nominal trajectory. This system is mainly developed for unmanned aircraft vehicles, where high levels of autonomy (i.e. the avoidance maneuver is autonomously executed by a standard autopilot) are required, but it can also be used, as aid to pilots, in manned commercial aircrafts. The effectiveness of the algorithm is evaluated by means of numerical simulations, where suitable conflict scenarios, taking into account aircraft dynamics and on-board sensors errors and limitations, are considered. Scenarios where both aircrafts are equipped with the proposed collision avoidance algorithm or where both aircrafts are subjected to Visual Flight Rules are also considered. 1 2

GN&C Technology Innovations for TAEM: USV DTFT2 Mission Results

This paper presents an overview of key GN&C technology innovations for Terminal Area Energy Management that have been flight demonstrated in CIRA’s Unmanned Space Vehicle Dropped Transonic Flight Test 2, successfully performed in April 2010. During the flight the vehicle dropped from an altitude of about 25 km, accelerated up to Mach 1.2 and then decelerated to less than Mach 0.2 at 5 km of altitude for final recovery using a very low cost parachute. In this paper, CIRA GN&C technology innovation roadmap for TAEM phase of flight is firstly described. Then an overview of innovations on guidance, navigation and control algorithms introduced in DTFT2 mission is given. Finally, a comparison between expected and actual flight test results is presented together with a discussion of the lessons learned.

Unscented Kalman Filtering for Reentry Vehicle Identification in the Transonic Regime

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.

Advanced GN&C Technologies for TAEM: Flight Test Results of the Italian Unmanned Space 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.

Automatic Guidance through 4D Waypoints with time and spatial margins

This paper presents a new algorithm for 4D Automatic Guidance, enabling the automatic capture of 4D waypoints (i.e. 3D points in the space with a requested time of arrival), while satisfying navigation constraints coming from both Air Traffic Management (i.e. time and space constraints on the waypoints to reach) and vehicle performance limitations. The proposed guidance strategy mainly relies on the continuous re-generation of the geometric reference trajectory and, in case this is not sufficient to guarantee the requested time of arrival, airspeed reference is adjusted to compensate for the difference between estimated and requested time of arrival of waypoint. The effectiveness of the proposed approach has been demonstrated by means of numerical simulations, including vehicle model and simplified autopilot/autothrust system that ensure tracking of the reference 4D trajectory. This paper was developed in the framework of 4DCoGC, a research project funded by European Commission under the Seventh Framework Programme whose objective is to address the aircraft 4D guidance and control principle.

Identification of the Transonic Aerodynamic Model for a Re-Entry Vehicle

Abstract The development of flight control systems for aerospace vehicles requires the availability of reliable aerodynamic models. Pre-flight models, obtained by means of wind tunnel tests and computational fluid dynamics analyses, are usually to be refined using test flight data in order to reduce the level of uncertainty on aerodynamic coefficients. In this paper we present a methodology for the estimation of the lateral-directional aerodynamic model of a re-entry vehicle in subsonic, transonic and supersonic regimes. The identification is formulated as a nonlinear filtering problem and solved through a multi-step approach using the Unscented Kalman Filter. The exploitation of all the available a priori information for the stochastic characterization of the filter models and sensors noises and a rigorous management of all the uncertainties involved in the system identification process allow to obtain reliable figures of estimation accuracy, that are of paramount importance for the design of guidance, navigation and control (GNC) system. The methodology is applied to the simulated data of the CIRA Dropped Transonic Flight Test 2 mission, that will be performed by the end of 2009, with the objective of refining the lateral-directional aerodynamic model.

An SFDI observer-based scheme for a general aviation aircraft

Abstract The problem of detecting and isolating sensor faults (sensor fault detection and isolation-SFDI) on a general aviation aircraft, in the presence of external disturbances, is considered. The proposed approach consists of an extended Kalman observer applied to an augmented aircraft plant, where some integrators are added to the output variables subject to faults. The output of the integrators should be ideally zero in the absence of model uncertainties, external disturbances and sensor faults. A threshold-based decision making system is adopted where the residuals are weighted with gains coming from the solution to an optimization problem. The proposed nonlinear observer was tested both numerically on a large database of simulations in the presence of disturbances and model uncertainties and on input-output data recorded during real flights. In this case, the possibility of successfully applying the proposed technique to detect and isolate faults on inertial and air data sensors, modelled as step or ramp signals artificially added to the real measurements, is shown.

Identification from Flight Data of the Aerodynamics of an Experimental Re-Entry Vehicle

Post flight data analyses are essential activities in aerospace projects. In particular, there is a specific interest in obtaining vehicle aerodynamic characteristics from flight data, especially for re-entry vehicle, in order to better understand theoretical predictions, to validate windtunnel test results and to get more accurate and reliable mathematical models for the purpose of simulation, stability analysis, and control system design and evaluation. Indeed, due to atmospheric re-entry specificity in terms of environment and phenomena, ground based experiments are not fully exhaustive and in-flight experimentation is mandatory. Moreover pre-flight models are usually characterised by wide uncertainty ranges, which should be reduced. These objectives can be reached by performing vehicle’s model identification from flight data.