Abstract

The ability to meet a controlled time of arrival during a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultane- ously maintaining airport throughput. However, if the wind fore- cast used to compute the initial trajectory plan is not accurate enough, the guidance system will need to correct time deviations from the plan during the execution of the descent. Previous work proposed an on-board guidance strategy based on model predictive control, which repeatedly updates the trajectory plan in real-time from the current aircraft state and for the remainder of the descent. However, the wind conditions downstream, at altitudes not explored yet, were difficult to predict due to the lack of data. This paper shows the potential benefits of using wind observations, broadcast by nearby aircraft, to reconstruct the wind profile downstream. The wind profile in the trajectory opti- mization problem is modeled as a spline, which control points are updated to fit the observations before re-planning the trajectory. Results from simulations using realistic wind data show that the performance of model predictive control significantly improves when including up-to-date wind observations, in terms of time and energy errors at the metering fix and fuel consumption. Peer Reviewed

Abstract

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 37th DASC Best student paper award,2018; premi al millor article de la conferència on el primer autor és un estudiant, atorgat per l'Institute of Electrical and Electronics Engineers Trajectory predictors require information on the flight-intent in order to estimate the future state of the aircraft. At present, however, such information is not available or it is very limited and coarse (unless predicting the ownship trajectory). In this paper, an interacting multiple-model (IMM) algorithm is proposed to improve the accuracy of short-term trajectory predictions. The active guidance mode of an aircraft is estimated in real-time observing flight data collected only from automatic dependent surveillance-Broadcast (ADS-B) and transponder selective mode (Mode S) emissions. The algorithm is set up with different models corresponding to the most typical guidance modes, and provides the model that better fits the observations. The proposed algorithm is validated by means of two simulated trajectories whose guidance modes were known beforehand. Finally, the performance of the algorithm with real flight data is demonstrated through a detailed example. Promising results are obtained, showing that the active guidance mode can be unequivocally identified with a negligible delay. Peer Reviewed Award-winning Document type: Conference object

Abstract

Trajectory prediction and optimisation algorithms will be the keystone for a successful trajectory based operations concept implementation, where accurate predictions and optimal trajectories will be needed for a wide variety of look-ahead times and operational contexts. The main goal of this paper is to present the architecture and capabilities of an aircraft trajectory prediction and optimisation framework suitable for various air traffic management research applications. The flexibility of this framework, called DYNAMO, allows for an easy implementation and assessment of actual and future concepts of operation, considering at the same time realistic weather data and aircraft performance models. In addition, its design enables the use for real-time applications and when a large set of trajectories needs to be rapidly generated for simulation and benchmarking purposes. The performance of the framewor Peer Reviewed

Abstract

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works 37th DASC Best of session (ATM-L: TMA Operations) award, 2018; concedit per l'Institute of Electrical and Electronics Engineers. The ability to meet a controlled time of arrival during a continuous descent operation will enable environmentally friendly and fuel efficient descent operations while simultaneously maintaining airport throughput. Previous work showed that guidance strategies based on a frequent recalculation of the optimal trajectory during the descent result in excellent environmental impact mitigation figures while meeting operational constraints in the presence of modelling errors. However, the time lag of recalculating the trajectory using traditional optimisation algorithms could lead to performance degradation and stability issues. This paper proposes an alternative strategy, which allows for fast updates of the optimal trajectory based on parametric sensitivities. Promising results show that the performance of this method is comparable to that of instantaneously recalculating the optimal descent trajectory at each time sample. Peer Reviewed Award-winning Document type: Conference object

Abstract

ming to a more collaborative demand and capacity balancing (DCB), in the scope of trajectory based operations, this paper presents an approach that takes alternative trajectories into a DCB optimization algorithm. These alternative trajectories are generated by the airspace users for those flights traversing hotspots (i.e. sectors with demand above capacity), which are predicted by the Network Manager. The trajectories consider lateral re-routings and/or vertical avoidance of all detected hotspots, which, along with different types of delay measures (including linear holding and in-flight delay recovery), are then integrated as a whole into a centralized optimization model to manage the traffic flow under a set of static scheme of airspace capacities. The combination of trajectory options and distribution of delays are hence optimized with the objective of minimizing the total deviation with regard to airspace users’ preferences (taking into account the fuel consumption, route charge and the cost of delay). Results suggest that delays can be remarkably reduced once alternative trajectory options are included in the DCB algorithm. Nevertheless, this delay reduction is obtained by diverting a large number of flights, yielding to an interesting trade-off between environmental impact and cost-efficiency for the airspace users. Peer Reviewed