Air control method based on reinforcement learning and four-dimensional trajectory

Abstract

The invention discloses an air control method based on reinforcement learning and a four-dimensional trajectory. The method comprises the following steps: firstly, establishing aerodynamic performance models of aircrafts of different types; acquiring four-dimensional trajectory data of different airlines for different airlines according to the aerodynamic performance model of the aircraft; through data playback, generating a four-dimensional trajectory model of an airline-aircraft model; and finally, based on a reinforcement learning algorithm, establishing a neural network, training a four-dimensional trajectory on the movement of the aircraft, constructing a nested reinforcement learning model of a nested speed agent in a course agent, and selecting an aircraft route by choosing a target course of the aircraft. The arrival time of the aircraft is controlled by selecting the target speed of the aircraft, and therefore the function that the aircraft presses the four-dimensional trajectory model according to the specified time, speed, course and height is achieved. According to the invention, a feasible solution can be provided for the problems of large flow, complex aircraft scheduling method, difficulty in air control and the like of the current airport.

Description

technical field [0001] The invention relates to the technical field of intelligent air traffic control, in particular to an air traffic control method based on reinforcement learning and four-dimensional trajectory. Background technique [0002] The new generation of air traffic control should be intelligent. This is due to the fact that the high density of traffic and the large number of aircraft presents great challenges to air traffic controllers (ATCos), who therefore require automated approaches to reduce complexity, especially during landings (arrivals) and takeoffs. A simple way to automate the air traffic control problem is to fly an aircraft along a calculated 4D trajectory by AI ATCos. [0003] EATA has identified data-driven trajectory prediction as one of the key pillars of future air traffic management, where trajectory in particular refers to the 4D trajectory typically predicted using models of aircraft aerodynamic performance. It highlights the importance o...

AI Technical Summary

Problems solved by technology

[0013] In view of the above problems, the object of the present invention is to provide a method of air control based on reinforcement learning and four-dimensional trajectory, which can provide a feasible solution to the current airports with large traffic, complex aircraft scheduling methods and difficult air control.

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