An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs

IF 2.1 3区 工程技术 Q2 ENGINEERING, AEROSPACE
Linfei Hou, Honglin Liu, Ting Yang, Shuaibin An, Rui Wang
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Abstract

In addressing the morphing problem in vehicle flight, some scholars have primarily employed reinforcement learning methods to make morphing decisions based on task. However, they have not considered the constraints associated with the task process. The innovation of this article is that it proposes an intelligent morphing decision method based on deep neural networks (DNNs) for the autonomous morphing decision problem of hypersonic boost-glide morphing vehicles under process constraints. Firstly, we established a dynamic model of a hypersonic boost-glide morphing vehicle with a continuously variable sweep angle. Then, in order to address the decision optimality problem considering errors and the heat flux density constraint problem during the gliding process, interference was introduced to the datum trajectory in segments. Subsequently, re-optimization was performed to generate a trajectory sample library, which was used to train an intelligent decision-maker using a DNN. The simulation results demonstrated that, compared with the conventional programmatic morphing approach, the intelligent morphing decision maker could dynamically determine the sweep angle based on the current flight state, leading to improved range while still adhering to the heat flux density constraint. This validates the effectiveness and robustness of the proposed intelligent decision-maker.
基于 DNN 的高超音速助推滑翔飞行器智能自主变形决策方法
在解决飞行器飞行中的变形问题时,一些学者主要采用强化学习方法来根据任务做出变形决策。但是,他们并没有考虑任务过程中的相关约束条件。本文的创新之处在于,针对过程约束下高超音速助推滑翔变形飞行器的自主变形决策问题,提出了一种基于深度神经网络(DNN)的智能变形决策方法。首先,我们建立了一个扫掠角连续可变的高超声速助推-滑翔变形飞行器动态模型。然后,为了解决滑行过程中考虑误差的决策优化问题和热通量密度约束问题,对基准轨迹分段引入干扰。随后,重新优化生成轨迹样本库,并利用 DNN 训练智能决策者。仿真结果表明,与传统的程序化变形方法相比,智能变形决策器可以根据当前飞行状态动态确定扫描角度,从而在遵守热通量密度约束的同时提高航程。这验证了所提出的智能决策制定器的有效性和稳健性。
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来源期刊
Aerospace
Aerospace ENGINEERING, AEROSPACE-
CiteScore
3.40
自引率
23.10%
发文量
661
审稿时长
6 weeks
期刊介绍: Aerospace is a multidisciplinary science inviting submissions on, but not limited to, the following subject areas: aerodynamics computational fluid dynamics fluid-structure interaction flight mechanics plasmas research instrumentation test facilities environment material science structural analysis thermophysics and heat transfer thermal-structure interaction aeroacoustics optics electromagnetism and radar propulsion power generation and conversion fuels and propellants combustion multidisciplinary design optimization software engineering data analysis signal and image processing artificial intelligence aerospace vehicles'' operation, control and maintenance risk and reliability human factors human-automation interaction airline operations and management air traffic management airport design meteorology space exploration multi-physics interaction.
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