Four-dimensional trajectory based operation(4D-TBO)is believed to enhance the planning and execution of efficient flights,reduce potential conflicts and resolve upcoming tremendous flight demand.Most of the 4D traject...Four-dimensional trajectory based operation(4D-TBO)is believed to enhance the planning and execution of efficient flights,reduce potential conflicts and resolve upcoming tremendous flight demand.Most of the 4D trajectory planning related studies have focused on manned aircraft instead of unmanned aerial vehicles(UAVs).This paper focuses on planning conflict-free 4D trajectories for fixed-wing UAVs before the departure or during the flight planning.A 4D trajectory generation technique based on Tau theory is developed,which can incorporate the time constraints over the waypoint sequence in the flight plan.Then the 4D trajectory is optimized by the particle swarm optimization(PSO)algorithm.Further simulations are performed to demonstrate the effectiveness of the proposed method,which would offer a good chance for integrating UAV into civil airspace in the future.展开更多
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...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.展开更多
A point-to-point iterative learning control method with the current-cycle feedback is proposed to enable aircraft to achieve an accurate four-dimensional(4D) trajectory tracking. To this end,the 4D trajectory tracking...A point-to-point iterative learning control method with the current-cycle feedback is proposed to enable aircraft to achieve an accurate four-dimensional(4D) trajectory tracking. To this end,the 4D trajectory tracking control problem is formulated into a point-to-point tracking control issue with an external disturbance. Then,the optimal point-to-point iterative learning control law is derived based on the successive projection method. Further,the current-cycle feedback error is added to the control law,so that the tracking error is reduced in both time and iteration domains. Finally,a numerical simulation is carried out using the kinematic model of an unmanned aerial vehicle and 4D trajectory data. Obtained results demonstrate that the proposed method can quickly reduce the trajectory tracking error even in the presence of gust interferences. Compared with the commonly used average velocity method and the velocity correction method,the proposed method makes full use of the past and current running data,and can continuously improve the accuracy of 4D trajectory tracking with the repetitive operation of aircraft between city pairs.展开更多
文摘Four-dimensional trajectory based operation(4D-TBO)is believed to enhance the planning and execution of efficient flights,reduce potential conflicts and resolve upcoming tremendous flight demand.Most of the 4D trajectory planning related studies have focused on manned aircraft instead of unmanned aerial vehicles(UAVs).This paper focuses on planning conflict-free 4D trajectories for fixed-wing UAVs before the departure or during the flight planning.A 4D trajectory generation technique based on Tau theory is developed,which can incorporate the time constraints over the waypoint sequence in the flight plan.Then the 4D trajectory is optimized by the particle swarm optimization(PSO)algorithm.Further simulations are performed to demonstrate the effectiveness of the proposed method,which would offer a good chance for integrating UAV into civil airspace in the future.
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).
文摘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.
基金supported by the Fundamental Research Funds for the Central Universities(No. 3122019131)。
文摘A point-to-point iterative learning control method with the current-cycle feedback is proposed to enable aircraft to achieve an accurate four-dimensional(4D) trajectory tracking. To this end,the 4D trajectory tracking control problem is formulated into a point-to-point tracking control issue with an external disturbance. Then,the optimal point-to-point iterative learning control law is derived based on the successive projection method. Further,the current-cycle feedback error is added to the control law,so that the tracking error is reduced in both time and iteration domains. Finally,a numerical simulation is carried out using the kinematic model of an unmanned aerial vehicle and 4D trajectory data. Obtained results demonstrate that the proposed method can quickly reduce the trajectory tracking error even in the presence of gust interferences. Compared with the commonly used average velocity method and the velocity correction method,the proposed method makes full use of the past and current running data,and can continuously improve the accuracy of 4D trajectory tracking with the repetitive operation of aircraft between city pairs.
