LEE- Uçak ve Uzay Mühendisliği Lisansüstü Programı

Bu topluluk için Kalıcı Uri

http://hdl.handle.net/11527/19473

Gözat

Konu "Air traffic control" ile LEE- Uçak ve Uzay Mühendisliği Lisansüstü Programı'a göz atma
  • Öge
    Optimization based-control of cooperative and noncooperative multi aircraft systems
    ( 2020) Başpınar, Barış ; Koyuncu, Emre ; 625456 ; Uçak ve Uzay Mühendisliği
    In this thesis, we mainly focus on developing methods that ensure autonomous control of cooperative and noncooperative multi-aircraft systems. Particularly, we focus on aerial combat, air traffic control problem, and control of multiple UAVs. We propose two different optimization-based approaches and their implementations with civil and military applications. In the first method, we benefit from hybrid system theory to present the input space of decision process. Then, using a problem specific evaluation strategy, we formulate an optimization problem in the form of integer/linear programming to generate optimal strategy. As a second approach, we design a method that generates control inputs as continuous real valued functions instead of predefined maneuvers. In this case, we benefit from differential flatness theory and flatness-based control. We construct optimization problems in the form of mixed-integer linear programming (MILP) and non-convex optimization problem. In both methods, we also benefit from game theory when there are competitive decision makers. We give the details of the approaches for both civil and military applications. We present the details of the hybrid maneuver-based method for air-to-air combat. We use the performance parameters of F-16 to model the aircraft for military applications. Using hybrid system theory, we describe the basic and advanced fighter maneuvers. These maneuvers present the input space of the aerial combat. We define a set of metrics to present the air superiority. Then, the optimal strategy generation procedure is formulated as a linear program. Afterwards, we use the similar maneuver-based optimization approach to model the decision process of the air traffic control operator. We mainly focus on providing a scalable and fully automated ATC system and redetermining the airspace capacity via the developed ATC system. Firstly, we present an aircraft model for civil aviation applications and describe guidance algorithms for trajectory tracking. These model and algorithms are used to simulate and predict the motion of the aircraft. Then, ATCo's interventions are modelled as a set of maneuvers. We propose a mapping process to improve the performance of separation assurance and formulate an integer linear programming (ILP) that benefits from the mapping process to ensure the safety in the airspace. Thereafter, we propose a method to redetermine the airspace capacity. We create a stochastic traffic environment to simulate traffics at different complexities and define breaking point of an airspace with regards to different metrics. The approach is validated on real air traffic data for en-route airspace, and it is shown that the designed ATC system can manage traffic much denser than current traffic. As a second approach, we develop a method that generates control inputs as continuous real valued functions instead of predefined maneuvers. It is also an optimization-based approach. Firstly, we focus on control of multi-aircraft systems. We utilize the STL specifications to encode the missions of the multiple aircraft. We benefit from differential flatness theory to construct a mixed-integer linear programming (MILP) that generates optimal trajectories for satisfying the STL specifications and performance constraints. We utilize air traffic control tasks to illustrate our approach. We present a realistic nonlinear aircraft model as a partially differentially flat system and apply the proposed method on managing approach control and solving the arrival sequencing problem. We also simulate a case study with a quadrotor fleet to show that the method can be used with different multi-agent systems. Afterwards, we use the similar flatness-based optimization approach to solve the aerial combat problem. In this case, we benefit from differential flatness, curve parametrization, game theory and receding horizon control. We present the flat description of aircraft dynamics for military applications. We parametrize the aircraft trajectories in terms of flat outputs. By the help of game theory, the aerial combat is modeled as an optimization problem with regards to the parametrized trajectories. This method allows the presentation of the problem in a lower dimensional space with all given and dynamical constraints. Therefore, it speeds up the strategy generation process. The optimization problem is solved with a moving time horizon scheme to generate optimal combat strategies. We demonstrate the method with the aerial combats between two UAVs. We show the success of the method through two different scenarios.