Identification of a Cessna Citation II Model Based on Flight Test Data

As a result of new aviation legislation, from 2019 on all air-carrier pilots are obliged to go through flight simulator-based stall recovery training. For this reason the Control and Simulation division at Delft University of Technology has set up a task force to develop a new methodology for high-fidelity aircraft stall behavior modeling and simulation. As part of this research project, the development of a new high-fidelity Cessna II simulation model, valid throughout the normal, pre-stall flight envelope, is presented in this paper. From an extensive collection of flight test data, aerodynamic model identification was performed using the Two-Step Method. New in this approach is the use of the Unscented Kalman Filter for an improved accuracy and robustness of the state estimation step. Also, for the first time an explicit data-driven model structure selection is presented for the Citation II by making use of an orthogonal regression scheme. This procedure has indicated that most of the six non-dimensional forces and moments can be parametrized sufficiently by a linear model structure. It was shown that only the translational and lateral aerodynamic force models would benefit from the addition of higher order terms, more specifically the squared angle of attack and angle of sideslip. The newly identified aerodynamic model was implemented into an upgraded version of the existing simulation framework and will serve as a basis for the integration of a stall and post-stall model.Control & Simulatio

State estimation in freeway traffic systems

Freeway networks are generally equipped with different types of sensors which are able to measure traffic conditions in real time. Such sensors are placed in fixed positions on the road network and, hence, measure traffic variables in specific positions, often far from each other, because their number is limited by technological and financial issues. In addition, the measurements provided by traffic sensors can be noisy and affected by failures. On the other hand, for designing efficient traffic control and monitoring systems, it is required to know the values of the traffic variables (flow, density, speed) on the different road segments, in real time. For these reasons, the problem of traffic estimation is quite relevant and has attracted the attention of researchers in the past decades. Such a problem will have to face new challenges in the near future, due to the fast development of intelligent and connected vehicles, which are able to measure traffic states and to transmit them in real time. These new technologies will enable much more traffic information than in the past, but providing mobile data that are, by nature, disaggregated and asynchronous