Adaptive self-governed aerial ecosystem by negotiated traffic
- Autores: Marko Radanovic
- Directores de la Tesis: Miquel Àngel Piera i Eroles (dir. tes.)
- Lectura: En la Universitat Autònoma de Barcelona ( España ) en 2018
- Idioma: español
- Tribunal Calificador de la Tesis: Romualdo Ortiz Moreno (presid.), Xavier Prats Menéndez (secret.), Francisco Javier Sáez Nieto (voc.)
- Programa de doctorado: Programa de Doctorado en Ingeniería Electrónica y de Telecomunicación por la Universidad Autónoma de Barcelona
- Materias:
- Enlaces
- Tesis en acceso abierto en: TDX
- Resumen
In recent years, several important research projects under the Single European Sky Air Traffic Management (ATM) Research and the Next Generation Air Transportation System initiatives, addressing the automation in ATM have been conducted. Those initiatives have envisaged the automation as a process driven by the overall ATM performance, focused on the system objectives and limitations. In a broader scope, the objectives are defined as a provision of the required separation between aircraft to meet the safety target levels, while the traffic competitiveness is maintained by means of an efficient system, environmentally friendly and socially valuable.
An increased operational density together with a lack of the air traffic control (ATC) capacity, in handling a higher traffic complexity, essentially imposes a separation provision to be implemented as cooperative and distributed, engaging also the airspace users (AUs). In this context, it is necessary to shift from a purely centralized tactical intervention model towards a more efficient strategic planning and proactive tactical operations, which assume significant changes of the roles and responsibilities of all involved stakeholders. That anticipates an operationally seamless integration of the safety net mechanisms and procedures in such a way that any pair of aircraft involved in a conflict, together with the surrounding traffic aircraft, behave as a stable and efficient, conflict-free air traffic system.
The research work in this thesis elaborates a novel safety net framework relying on the concept of aerial ecosystems to transform the non-coordinated targets between separation management at the tactical level and collision avoidance at the operational level, into a cooperative and efficient, conflict-free system. The aerial ecosystems can be understood as a paradigm of the complex adaptive systems, in which aircraft trajectories change and evolve over time because of interactions among involved aircraft and its ever-changing environment. The thesis comprises few analytical outcomes utilized by the means of quantitative methods for identification of the spatiotemporal interdependencies and computation of the total ecosystem-level solutions and deadlock within available ecosystem time. The analytical methods are applications-oriented rather than a theory-based, developed with a quantitative and discrete modeling approach, and customized to the current traffic demands and the operational environment.
As a result, the ecosystem framework has an ability to further explore the potential resolution capacity in a search space of the system solutions. A decreasing rate of the available ecosystem resources and an elapsed time describe a potential path in an explicit determination of the resolution dynamics, meaning that each missed moment in making a resolution agreement might reprobate in a less number of the conflict-free maneuvers, but also maintain or increase them in some circumstances. The approach has shown a significance in providing the time capacity for a set of certain maneuvers at the operational level, when a severity of the conflict situation occurs very rapidly. With a causal increment in a number of ecosystem aircraft and diverse trajectory geometries, the structure of spatiotemporal interdependencies becomes larger which can produce less resolution capacity and a shorter decision-making time.
The modeling methodology can be deployed as both the airborne and ground-based decision support tool. Follow-up research will be multi-directionally formalized throughout conceptual advancement of the resolution regions, integration of the aircraft performance models, development of a machine learning model for the surrounding traffic complexity, and implementation of the cooperative and competitive ecosystems for unmanned aerial vehicles.
