In this study, an intelligent predictor-corrector entry guidance approach for lifting hypersonic vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the deep neural network (DNN) and constraint management techniques. First, the entry trajectory planning problem is formulated as a univariate root-finding problem based on a compound bank angle corridor, and two constraint management algorithms are presented to enforce the satisfaction of both path and terminal constraints. Second, a DNN is developed to learn the mapping relationship between the flight states and ranges, and experiments are conducted to verify its high approximation accuracy. Based on the DNN-based range predictor, an intelligent, multi-constrained predictor-corrector guidance algorithm is developed to achieve real-time trajectory correction and lateral heading control with a determined number of bank reversals. Simulations are conducted through comparing with the state-of-the-art predictor-corrector algorithms, and the results demonstrate that the proposed DNNbased entry guidance can achieve the trajectory correction with an update frequency of 20 Hz and is capable of providing highprecision, safe, and robust entry guidance for hypersonic vehicles.

中文翻译：

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使用深度神经网络的多约束实时进入指导

在这项研究中，提出了一种用于提升高超音速飞行器的智能预测器-校正器进入引导方法，通过利用深度神经网络（DNN）和约束管理技术实现对进入飞行的实时和安全控制。首先，入口轨迹规划问题被表述为基于复合坡度角走廊的单变量寻根问题，并提出了两种约束管理算法来强制满足路径和终端约束。其次，开发了一个 DNN 来学习飞行状态和范围之间的映射关系，并进行实验以验证其高近似精度。基于基于 DNN 的范围预测器，一个智能的、开发了多约束预测器-校正器制导算法，以实现实时轨迹校正和横向航向控制，并具有确定的倾斜倒转次数。通过与最先进的预测器-校正器算法进行比较进行仿真，结果表明所提出的基于 DNN 的入口引导可以实现更新频率为 20 Hz 的轨迹校正，并且能够提供高精度、安全、和强大的高超音速飞行器入门指南。