Near-Optimal Control for Aircraft Conflict Resolution in the Presence of Uncertainty

Abstract

In this paper, a stochastic near-optimal control method is proposed for determining aircraft conflict-resolution trajectories in the presence of uncertainty in real time. The prior work developed a stochastic optimal control method for aircraft conflict resolution based on the polynomial chaos expansion and pseudospectral methods. This stochastic optimal control method is extended to generate conflict-resolution trajectories in real time without actually solving the computationally expensive stochastic optimal control problems. The proposed near-optimal conflict-resolution algorithm is based on a recently developed surrogate modeling technique called polynomial chaos kriging, which is used to construct the surrogate models of the optimal conflict-resolution trajectories from a set of precomputed optimal solutions. The near-optimal conflict-resolution trajectories can be accurately generated in real time from the surrogate models with the information of current conditions (for example, current states). Through illustrative aircraft conflict-resolution examples, the performance and effectiveness of the proposed stochastic near-optimal conflict-resolution algorithm are evaluated and demonstrated.

Robust Aircraft Trajectory Planning Under Wind Uncertainty Using Optimal Control

“…However, there is another relevant difference between trajectories: as the penalty in uncertainty grows, the optimal airspeed when the aircraft crosses uncertain regions increases. We attribute this result to the idea that, by flying at a higher speed, the relative importance of the wind on the groundspeed is reduced 15 . These airspeed changes reduce uncertainty more efficiently than direct avoidance, as it can be observed in Figure 10.…”

“…It is efficient for problems with low-dimensional random variables [28], but the number of required nodes grows quickly with the dimension of the uncertain parameters even when using higher-efficiency sparse grids [29]. It can also be combined with kriging techniques [15].…”

“…routed in an environment with uncertain threats; in [15], it is employed for conflict-free 4D trajectory optimization.…”

Factorial Experiment on Route Structures Using SAAM on a Specified Area in ASEAN

“…The differential evolution algorithm was used to optimize the potential field (PF) and proved that PFDE performed better than PF [14]. Matsuno et al proposed the nearoptimal control method to resolve the aircraft conflict based on the polynomial chaos expansion and pseudospectral methods [15]. Due to the nature of conflicts reduction from our ATS routes improvement, it can also be one of the solutions for conflict resolutions.…”

Planificación de trayectorias 4D de aeronaves en entornos multiobjetivo

“…Valenzuela y Rivas [155] ofrecieron un método para la detección y resolución de conflictos basado en el uso de patrones de trayectoria predefinidos. Matsuno et al [103] propusieron un método de control estocástico casi óptimo para determinar las trayectorias de resolución de conflictos de aeronaves en presencia de incertidumbre en tiempo real. Durand y Barnier [47] experimentaron con un algoritmo derivado de la robótica para defender el control centralizado del tráfico aéreo.…”