Near-Optimal Conflict-Free Trajectory Generation 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 (e.g., 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.