基于深度神经网络的变比冲小推力交会实时最优控制

Real-Time Optimal Control for Variable-Specific-Impulse Low-Thrust Rendezvous via Deep Neural Networks

针对燃料最优交会问题,提出了一种基于深度神经网络的实时控制方法。首先,发展了面向燃料最优交会问题的轨迹反向生成方法,该方法基于现有的反向生成思想进行二分法迭代,以满足反向积分的两个截止条件。然后,构造了一种适用于变比冲模型的深度神经网络结构,并将网络的输出控制分为推力输出和比冲输出。提出了先学习最优比冲,然后根据比冲的实际上下限约束对其进行限制以获得比冲输出的方法。进一步,通过设计增强容错深度神经网络以提高交会任务末端接近段的鲁棒性。最后,通过对地球至阿波菲斯小行星和地球至火星的任务仿真,验证了所提方法的有效性和高效性。

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.