Transactions of Nanjing University of Aeronautics & Astronautics
Real-Time Optimal Control for Variable-Specific-Impulse Low-Thrust Rendezvous via Deep Neural Networks
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Abstract:
This paper presents a real-time control method based on deep neural networks （DNNs） for the fuel-optimal rendezvous problem. A backward generation optimal examples method for the fuel-optimal rendezvous problem is proposed， which iterates through the dichotomy method based on the existing backward generation idea while satisfying the two integration cutoff conditions of the backward integration. We construct a DNNs structure suitable for the variable-specific-impulse model and divide the output control of networks into the thrust output and the specific impulse output. For the specific impulse output， a method is proposed that learns the optimal specific impulse first and then limits it according to its actual upper and lower limits. We propose the enhanced fault-tolerant deep neural networks （EFT-DNNs） to improve the robustness when approaching rendezvous. The effectiveness and efficiency of the proposed method are verified by simulations of the Earth-Apophis asteroid and Earth-Mars missions.
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Project Supported:
This work was supported by the National Natural Science Foundation of China （No.12102177） and the Natural Science Foundation of Jiangsu Province （No.BK20220130）.
Clc Number:
V412.4
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