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An on-line deep learning framework for low-thrust trajectory optimisation

机译：低推力轨迹优化的在线深度学习框架

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摘要

In the preliminary interplanetary mission design stage, a fast low-thrust (LT) transfer cost approximator will improve the mission design efficiency and enable us to design more complex missions. In this study, we propose a Deep Neural Network (DNN) based on-line framework for training approximators for fast low-thrust trajectory optimisation. The online characteristic refers to the ability to continuously adjust the trained DNNs using new LT transfer data from newly found asteroids, which avoids repeated and costly re-training and is particularly useful for mission scenarios where new data are obtained regularly. The framework contains a DNN-classifier and an online-DNN regressor. The Bayesian optimisation (BO) technique is adapted to determine the network structure as well as the feature selection and processing methods. The proposed DNN-classifier significantly improves the LT optimisation convergence rate from 7% to over 98%. The proposed on-line DNN regressor proves to have better generalization ability and scalability comparing to series network structure, giving a mean relative error (MRE) of approximately 0.6% in the test Near-Earth Asteroid (NEA) rendezvous mission scenario. The proposed on-line DNN framework can also be extended to solve other trajectory optimisation problems in other mission scenarios, such as Main-Belt Asteroid (MBA) missions, active debris removal mission (ADR), etc. (c) 2021 Elsevier Masson SAS. All rights reserved.
机译：在初步行驶任务设计阶段，快速低推力（LT）转移成本近似器将提高任务设计效率，使我们能够设计更复杂的任务。在这项研究中，我们提出了一种基于深度神经网络（DNN）的基线框架，用于训练快速低推力轨迹优化的近似器。在线特性是指使用来自新发现的小行星的新的LT传输数据来连续调整训练的DNN的能力，这避免了重复和昂贵的重新训练，并且对于定期获得新数据的使命方案特别有用。该框架包含DNN-Classifier和在线DNN回归器。贝叶斯优化（BO）技术适用于确定网络结构以及特征选择和处理方法。所提出的DNN分类器显着提高了LT优化收敛速率从7％到超过98％。所提出的在线DNN回归主管证明，与串联网络结构相比，具有更好的泛化能力和可扩展性，在测试近地球（NEA）Rendezvous Missifario中，在测试近地球邻近的Asteroid（NEA）中的平均相对误差（MRE）约为0.6％。建议的在线DNN框架也可以扩展，以解决其他任务方案中的其他轨迹优化问题，例如主带小行星（MBA）任务，有效的碎片删除任务（ADR）等（C）2021 Elsevier Masson SAS 。版权所有。

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