用深度神经网络引导可重复使用运载火箭着陆的Oracle *

2022 International Conference on Control, Automation and Diagnosis (ICCAD) Pub Date : 2022-07-13 DOI:10.1109/ICCAD55197.2022.9854017

J. M. Igreja, J. M. Lemos

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引用次数: 1

摘要

oracle对于深度神经网络的训练至关重要。本文从线性化反馈控制律出发，建立了一种可重复使用运载火箭的着陆预测模型，该模型能完成规定的着陆轨迹跟踪。然后，甲骨文被用来训练一个深度神经网络，该网络可以用作着陆机动的制导系统。验证由蒙特卡罗执行。

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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Oracle for Guidance with Deep Neural Networks in Reusable Launch Vehicle Landing*

Oracles are of paramount importance for Deep Neural Networks training. In this paper, an oracle developed for landing reusable launch vehicles is created from a linearizing feedback control law that can perform a prescribed landing trajectory tracking. The oracle is then used to train a Deep Neural Network that can be used as a guidance system for landing maneuvers. Verification is performed by Monte-Carlo.

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来源期刊

2022 International Conference on Control, Automation and Diagnosis (ICCAD)

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