DDQNC-P:A framework for civil aircraft tactical synergetic trajectory planning under adverse weather conditions  

作　　者：Honghai ZHANG Jinlun ZHOU Zongbei SHI Yike LI Jinpeng ZHANG 

机构地区：[1]College of Civil Aviation,Nanjing University of Aeronautics&Astronautics,Nanjing 211106,China

出　　处：《Chinese Journal of Aeronautics》2024年第12期434-457,共24页中国航空学报（英文版）

基　　金：the support of the Chinese Special Research Project for Civil Aircraft(No.MJZ1-7N22);the National Natural Science Foundation of China(No.U2133207).

摘　　要：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 essential 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 synergetic 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 multiaircraft trajectory planning problem processable for the trained Agent.This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly.Substantial 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 3504D trajectory points per aircraft,finally confirming its applicability to make real-time decision-making support for controllers in real-world ATC systems.

关 键 词：Air traffic control Trajectory-based operation 4D trajectory planning Reinforcement learning Decision support systems 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] V355.1[自动化与计算机技术—控制科学与工程]