Georgios Chaloulos

Effect of Wind Correlation on Aircraft Conflict Probability

A study which examines the effect of wind correlation on aircraft conflict probability estimation is presented. We describe the correlation structure of the difference between the actual wind and the meteorological wind forecasts and discuss how it can be implemented in simulation. For several encounters we then examine the aircraft conflict probability estimation errors if the correlation structure is ignored and the wind is instead assumed to be modeled as white noise. The conclusion of the study is that wind correlation may have a significant effect under particular encounter geometries.

Distributed hierarchical MPC for conflict resolution in air traffic control

We present a decentralized Model Predictive Control scheme for hierarchical systems to tackle the collision avoidance problem for autonomous aircraft in an air traffic control setting. Using a low level controller, the aircraft dynamic equations are abstracted to simpler unicycle kinematic equations. The navigation function methodology is then used to generate conflict free trajectories for all aircraft. In order to ensure that the resulting trajectories respect the aerodynamic constraints of the aircraft, a decentralized model predictive controller is added at a higher level, to provide preview to the otherwise myopic navigation functions. The overall hierarchical, distributed control scheme has the same feasibility properties as the corresponding centralized scheme. Its performance is demonstrated by simulations of dense air traffic scenarios.

Control of multiple non-holonomic air vehicles under wind uncertainty using Model Predictive Control and decentralized navigation functions

We present a novel control scheme for multiple non-holonomic vehicles under uncertainty, which can guarantee collision avoidance while complying with constraints imposed on the vehicles. Dipolar navigation functions are used for decentralized conflict-free control, while model predictive control is used in a centralized manner in order to ensure that the resulting trajectories remain feasible with respect to the constraints present and to optimize the performance objectives. The model used is chosen to resemble air traffic control problems, with some uncertainty introduced in the system. The efficiency of the control strategy is demonstrated by realistic simulations.

Ground Assisted Conflict Resolution in Self-Separation Airspace

We propose a novel method for conflict resolution for aircraft flying in a self-separation airspace, with the possibility of some ground assistance. Our method is based on navigation functions, that can guarantee decentralized conflict avoidance for the short term horizon, supported by a model predictive controller on the ground, responsible for the optimization of the aircraft flight plans in the mid term horizon. As a consequence, our method combines the short term safety guarantees provided by navigation functions with the long term optimality and constraint satisfaction guarantees (in terms of airspeed, turning radius etc.) provided by model predictive controllers. The efficiency of the approach is demonstrated on simulations involving a number of aircraft converging in planar configurations.

Wind Uncertainty Correlation and Aircraft Conflict Detection Based on RUC-1 Forecasts

The purpose of this study is to examine the wind forecast uncertainty correlation and its efiect on aircraft con∞ict detection. The spatiotemporal correlation structure of the wind prediction uncertainty of Rapid Update Cycle - 60 (RUC-1) forecasts is described and it is discussed how it can be implemented for simulation purposes. We measure the impact of this correlation by examining for several encounters the aircraft con∞ict probability estimation errors in cases where this structure is partially or completely ignored and the wind is instead modeled as white noise. The study concludes that wind correlation may have a signiflcant efiect under particular encounter geometries; the efiect is most pronounced for planar encounters.

Hierarchical Control with Prioritized MPC for Conflict Resolution in Air Traffic Control

Abstract We present a hierarchical solution to the problem of collision avoidance in air traffic control utilizing priority-based Model Predictive Control. First, we abstract the physical aircraft dynamics to simplified ones. Then, we design a centralized model predictive controller that takes into account the physical limitations of the aircraft, such as input constraints and turning rates, as well as the minimum separation safety constraints among the aircraft. We include the effects of bounded winds on the simplified dynamics at the optimization level and show how to exploit the spatial correlation in the wind statistics in order to reduce the conservatism in the separation constraints. This results in a mixed-integer linear program that can be solved online for problems of realistic size. The obtained solution is translated through the flight management system to appropriate inputs to be applied to the physical aircraft dynamics. Monte Carlo simulations are provided to illustrate the effectiveness of the scheme.

On stochastic control up to a hitting time

We propose a dynamic programming-based solution to a stochastic optimal control problem up to a hitting time for a discrete-time Markov control process. Firstly, we determine an optimal control policy to steer the process toward a compact target set while simultaneously minimizing an expected discounted cost. We then provide a rolling-horizon strategy for approximating the optimal policy, together with quantitative characterization of its sub-optimality with respect to the optimal policy.