Trajectory generation and display for free flight

TL;DR

This paper introduces a neural network-based method for generating optimal aircraft trajectories to efficiently join a leader aircraft, demonstrated through simulations with wide-body aircraft.

Contribution

It presents a novel neural approximation approach for real-time trajectory generation in aircraft formation flying, combining optimal control theory with adaptive neural networks.

Findings

Neural networks effectively approximate optimal trajectories.

The method enables real-time trajectory generation during operation.

Simulation results validate the approach with wide-body aircraft.

Abstract

In this study a new approach is proposed for the generation of aircraft trajectories. The relative guidance of an aircraft, which is aimed to join in minimum time the track of a leader aircraft, is particularly considered. In a first place, a minimum time relative convergence problem is considered and optimal trajectories are characterized. Then the synthesis of a neural approximator for optimal trajectories is discussed. Trained neural networks are used in an adaptive manner to generate intent trajectories during operation. Finally simulation results involving two wide body aircraft are presented.