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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Concerned with the problem of interceptor midcourse guidance trajectory online planning satisfying multiple constraints, an online midcourse guidance trajectory planning method based on deep reinforcement learning (DRL) is proposed. The Markov decision process (MDP) corresponding to the background of a trajectory planning problem is designed, and the key reward function is composed of the final reward and the negative step feedback reward, which lays the foundation for the interceptor training trajectory planning method in the interactive data of a simulation environment; at the same time, concerned with the problems of unstable learning and training efficiency, a trajectory planning training strategy combined with course learning (CL) and deep deterministic policy gradient (DDPG) is proposed to realize the progressive progression of trajectory planning learning and training from satisfying simple objectives to complex objectives, and improve the convergence of the algorithm. The simulation results show that our method can not only generate the optimal trajectory with good results, but its trajectory generation speed is also more than 10 times faster than the hp pseudo spectral convex method (PSC), and can also resist the error influence mainly caused by random wind interference, which has certain application value and good research prospects.

Details

Title
Online Trajectory Planning Method for Midcourse Guidance Phase Based on Deep Reinforcement Learning
Author
Li, Wanli 1 ; Li, Jiong 2   VIAFID ORCID Logo  ; Li, Ningbo 3 ; Shao, Lei 2 ; Li, Mingjie 1 

 Graduate College, Air Force Engineering University, Xi’an 710051, China 
 Air Defense and Missile Defense College, Air Force Engineering University, Xi’an 710051, China 
 China Aerodynamics Research and Development Center, Mianyang 621000, China 
First page
441
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
22264310
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2819260424
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.