@inproceedings{2b540b3445e64f60896bc844a352e283,
title = "Time-Constrained Guidance Law for a Ground-Attack Missile with Time-Vary Speed",
abstract = "This paper investigates the issue of impact-time-constrained guidance problem for a gliding missile and proposes a machine learning-based approach. A three-hidden-layer Deep Neural Network (DNN) is adopted with each layer included 100 neurons to realize the accurate prediction of the time-to-go of proportional navigation guidance (PNG), and an analytical impact time constrained guidance (AITCG) law is developed using the outputs of the DNN. Then a bias term is developed to nullify the difference between the predicted time-to-go and its desired value. The main benefit of this approach lies in its accurate time-to-go prediction with DNN for a highly nonlinear system. Hence the impact time will be corrected in quite an efficient manner. Simulation results demonstrate that the trained DNN accurately estimates the time-to-go and the AITCG law can meet the need of the impact time control. Numerous Monte-Carlo simulations are performed to support our findings.",
keywords = "Computational Guidance, Gliding missile, Impact-time-control guidance, Missile guidance, Neural Network",
author = "Jinxi Yang and Jiang Wang and Hongyan Li and Zichao Liu and Zhichao Li",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 1st Aerospace Frontiers Conference, AFC 2024 ; Conference date: 12-04-2024 Through 15-04-2024",
year = "2024",
doi = "10.1117/12.3032600",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Han Zhang",
booktitle = "First Aerospace Frontiers Conference, AFC 2024",
address = "United States",
}