Reinforcement Learning-Based 3-D Sliding Mode Interception Guidance via Proximal Policy Optimization

Jianguo Guo, Mengxuan Li, Zongyi Guo, Zhiyong She

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

This article proposes a novel 3-D sliding mode interception guidance law for maneuvering targets, which explores the potential of reinforcement learning (RL) techniques to enhance guidance accuracy and reduce chattering. The guidance problem of intercepting maneuvering targets is abstracted into a Markov decision process whose reward function is established to estimate the off-target amount and line-of-sight angular rate chattering. Importantly, a design framework of reward function suitable for general guidance problems based on RL can be proposed. Then, the proximal policy optimization algorithm with a satisfactory training performance is introduced to learn an action policy which represents the observed engagements states to sliding mode interception guidance. Finally, numerical simulations and comparisons are conducted to demonstrate the effectiveness of the proposed guidance law.

源语言英语
页(从-至)423-430
页数8
期刊IEEE Journal on Miniaturization for Air and Space Systems
4
4
DOI
出版状态已出版 - 1 12月 2023

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