A Penetration Strategy for High-Speed Vehicles Based on Soft Actor-Critic

Xiaojie Zhang, Jiaolong Liu, Xiaoming Wang, Hang Guo, Wenxing Fu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In response to the limited adaptability of conventional evasive tactics, a Soft Actor-Critic (SAC) based penetration strategy for high-speed vehicles is proposed. The strategy is tailored for a three-dimensional (3D) combat scenario where two interceptors commence with stochastic initial postures. The reward function is designed to prioritize successful penetration while concurrently minimizing energy consumption and trajectory deviation during evasive maneuvers. The algorithm enables the high-speed vehicle to make online decisions based on real-time relative motions and the vehicle’s intrinsic status, issuing optimal overload commands for evasion. The guidance laws for altitude and heading tracking to return to the original trajectory are designed. The simulation results indicate that the proposed method is applicable to frontal encirclement interception scenarios under various initial conditions, achieving a success evasion rate of over 90% while adhering to energy constraints. Additionally, the method ensures that the vehicle maintains minimal deviation from its original trajectory post-evasion, facilitating a swift return to the initial flight path.

Original languageEnglish
Title of host publicationProceedings of 4th 2024 International Conference on Autonomous Unmanned Systems, 4th ICAUS 2024
EditorsLianqing Liu, Yifeng Niu, Wenxing Fu, Yi Qu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages390-401
Number of pages12
ISBN (Print)9789819635511
DOIs
StatePublished - 2025
Event4th International Conference on Autonomous Unmanned Systems, ICAUS 2024 - Shenyang, China
Duration: 19 Sep 202421 Sep 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1374 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference4th International Conference on Autonomous Unmanned Systems, ICAUS 2024
Country/TerritoryChina
CityShenyang
Period19/09/2421/09/24

Keywords

  • Deep Reinforcement Learning
  • High-speed vehicle
  • Maneuvering penetration

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