A time-coordination reentry guidance law using deep neural networks for Morphing aircrafts is developed in this paper. The neural network fits the mapping from states, guidance and morphing parameters to flight performances using the dataset generated by traversing bank angle profile and morphing parameters. In the guidance law, leveraging the automatic differentiation property of the neural network and Newton iteration methods, guidance and morphing parameters matching the expected range and flight time are determined. Lateral guidance is conducted based on the exponential convergence criterion for bank angle flips. Simulation results demonstrate that multiple morphing aircrafts satisfy path constraints and achieve the desired guidance accuracy, providing sufficient evidence for the effectiveness of the time-coordination guidance law.
Time-Coordination Entry Guidance for Unpowered Gliding Morphing Aircrafts Using Deep Neural Networks
Lect. Notes Electrical Eng.
International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024
2025-03-05
12 pages
Article/Chapter (Book)
Electronic Resource
English
AIAA | 1995
|Impact speed and angle constrained guidance law for unpowered gliding vehicle
Elsevier | 2025
|An Approximate Optimal Maximum Range Guidance Scheme for Subsonic Unpowered Gliding Vehicles
DOAJ | 2015
|Entry Guidance Using Time-Scale Separation in Gliding Dynamics
Online Contents | 2015
|