Abstract
Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning,which requires superior real-time performance and conflict-free reliability of solving methods.Multi-aircraft real-time 4D trajectory planning under adverse weather is an essen-tial problem in Air Traffic Control(ATC)and it is challenging for the existing methods to be applied effectively.A framework of Double Deep Q-value Network under the Critic guidance with heuristic Pairing(DDQNC-P)is proposed to solve this problem.An Agent for two aircraft syner-getic trajectory planning is trained by the Deep Reinforcement Learning(DRL)model of DDQNC,which completes two aircraft 4D trajectory planning tasks preliminarily under dynamic weather conditions.Then a heuristic pairing algorithm is designed to convert the multi-aircraft synergetic trajectory planning into multi-time pairwise synergetic trajectory planning,making the multi-aircraft trajectory planning problem processable for the trained Agent.This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly.Sub-stantial simulations with various aircraft numbers,weather conditions,and airspace structures were conducted for performance verification and comparison.The success rate of conflict-free trajectory resolution reached 96.56％with an average calculation time of 0.41 s for 350 4D trajectory points per aircraft,finally confirming its applicability to make real-time decision-making support for con-trollers in real-world ATC systems.
维普
万方数据