Deep Kernel-Based Optimal Control Prediction in Aerospace Missions

While deep learning has shown its promises in solving spacecraft optimal control problems, it still requires a large amount of training samples and is lack of the interpretability. In this article, we introduce the nonparametric flexibility of kernel approaches into the deep learning architecture, and develop a deep kernel learning model to provide an interpretable control prediction in various aerospace missions. In our framework, the core is a Gaussian process function with RBF kernel that can transform the latent features into infinite-dimensional latent space to obtain interpretable similarities. It hence intuitively brings the positive or negative correlations between the predicted result and all training samples. The parameters of both the deep architecture and the kernel are jointly trained in an end-to-end manner. Simulation results in two aerospace missions show that the proposed model outperforms the stand-alone deep neural network model and the Gaussian process model in control prediction accuracy and interpretability under insufficient training samples.