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Low-thrust spacecraft trajectory optimization via a DNN-based method

机译：通过基于DNN的方法的低推力航天器轨迹优化

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摘要

Initial solution guess has a significant impact on the convergence of indirect methods, especially for continuous low-thrust trajectory optimization problems. In this study, an intelligent initial solution supplying approach based on deep neural networks (DNNs) is proposed to help achieve the fast generation of optimal trajectories for low-thrust orbit transfers. Energy-optimal and fuel-optimal trajectories with three different terminal conditions are considered. Based on the training dataset obtained by an indirect method, DNNs are constructed to approximate the solutions corresponding to different flight states. Based on the trained DNNs, an intelligent trajectory optimization method named DNN-based method is developed with the help of the homotopy technique. Numerical simulations are conducted to evaluate the performance of the proposed method on success rates and time consumptions. Simulation results demonstrate that the combination of traditional techniques and the new DNN technology can achieve the fast generation of low-thrust optimal trajectories with advantages on computational efficiency and reliability.
机译：初始解决方案猜测对间接方法的收敛具有显着影响，特别是对于连续的低推力轨迹优化问题。在本研究中，提出了一种基于深度神经网络（DNN）的智能初始解决方案供应方法，以帮助实现低推力轨道转移的快速产生最佳轨迹。考虑了具有三种不同终端条件的能量最佳和燃料最佳轨迹。基于通过间接方法获得的训练数据集，构造DNN以近似于对应于不同飞行状态的解决方案。基于训练的DNN，在同型技术的帮助下，开发了一种基于DNN的方法的智能轨迹优化方法。进行了数值模拟，以评估提出的方法对成功率和时间消耗的性能。仿真结果表明，传统技术和新的DNN技术的组合可以实现具有关于计算效率和可靠性的优点的快速产生低推力最佳轨迹。

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