Intelligent model correction and trajectory planning for air-breathing hypersonic vehicle considering inlet unstart

IF 5 1区工程技术 Q1 ENGINEERING, AEROSPACE

Aerospace Science and Technology Pub Date : 2025-06-04 DOI:10.1016/j.ast.2025.110401

Jiaxin Li , Zhigang Wu , Yanqi Feng , Guan Wang , Kai Liu

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引用次数: 0

Abstract

This study develops a protection mechanism against inlet unstart for air-breathing hypersonic vehicles (AHVs) by predicting potential unstart scenarios using a mechanism model. A deep neural network (DNN)-based trajectory planner is employed to avoid unstart-triggering flight paths. AHV mechanism model is described, and error sources are analyzed. A reliable sensor feedback scheme is designed to correct model parameters using neural networks. The trajectory optimization problem is formulated as a highly nonlinear optimal control problem, with state-action vectors extracted from optimal trajectories generated from random initial states. A DNN is then trained to learn the relationship between flight states and optimal actions, enabling optimal action prediction. The key contribution of this study lies in integrating neural networks with mechanism model correction and trajectory optimization to prevent inlet unstart. The algorithm's effectiveness is validated through numerical simulations.

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考虑进气道起动的吸气式高超声速飞行器智能模型修正与轨迹规划

本研究通过使用机制模型预测潜在的不启动情况，开发了一种针对吸气式高超声速飞行器（ahv）进气道不启动的保护机制。采用基于深度神经网络（DNN）的轨迹规划器来避免未启动触发的飞行路径。描述了AHV机理模型，分析了误差来源。设计了一种可靠的传感器反馈方案，利用神经网络对模型参数进行校正。将轨迹优化问题表述为一个高度非线性的最优控制问题，从随机初始状态生成的最优轨迹中提取状态-作用向量。然后训练DNN来学习飞行状态和最优动作之间的关系，从而实现最优动作预测。本研究的关键贡献在于将神经网络与机制模型修正和轨迹优化相结合，以防止进气道不启动。通过数值仿真验证了该算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Aerospace Science and Technology 工程技术-工程：宇航

CiteScore

10.30

自引率

28.60%

发文量

654

审稿时长

54 days

期刊介绍： Aerospace Science and Technology publishes articles of outstanding scientific quality. Each article is reviewed by two referees. The journal welcomes papers from a wide range of countries. This journal publishes original papers, review articles and short communications related to all fields of aerospace research, fundamental and applied, potential applications of which are clearly related to: • The design and the manufacture of aircraft, helicopters, missiles, launchers and satellites • The control of their environment • The study of various systems they are involved in, as supports or as targets. Authors are invited to submit papers on new advances in the following topics to aerospace applications: • Fluid dynamics • Energetics and propulsion • Materials and structures • Flight mechanics • Navigation, guidance and control • Acoustics • Optics • Electromagnetism and radar • Signal and image processing • Information processing • Data fusion • Decision aid • Human behaviour • Robotics and intelligent systems • Complex system engineering. Etc.