This paper investigates the heliocentric time-optimal rendezvous performance of Sun-facing diffractive solar sails with various deflection angles and acceleration capabilities. Diffractive solar sails, which generate tangential radiation pressure force, are proposed and schematically designed to achieve diverse radiation pressure distributions. The radiation pressure force model and the time-optimal control problem for these innovative Sun-facing diffractive solar sails are established. Utilizing an indirect method and the optimal control law, we explore typical heliocentric rendezvous scenarios to assess the variational trends of transfer time in relation to different deflection angles and acceleration capabilities. The results for Sun-facing diffractive sails in specific rendezvous missions are compared to reflective sails with the same area-to-mass ratio, focusing on transfer trajectory and attitude control. Our findings reveal that diffractive sails exhibit significant advantages over reflective sails, particularly in the context of normal acceleration, paving the way for more efficient space exploration.

This paper proposed a new attitude determination method for low-orbit spacecraft. The attitude prediction accuracy is greatly improved by adding the unmodeled environmental torque to the dynamic equation. Specifically, the environmental torque extraction algorithm based on extended Kalman filter and series extended state observer is introduced, and the unmodeled part of dynamic is identified through the inverse dynamic model. Then, the collected data are analyzed and trained by a backpropagation neural network, resulting in an attitude-torque mapping network with compensation ability. The simulation results show that the proposed feedback attitude prediction algorithm can outperform standard methods and provide a high accurate picture of prediction and reliability with discontinuous measurement.

Real-time guidance is critical for the vertical recovery of rockets. However, traditional sequential convex optimization algorithms suffer from shortcomings in terms of their poor real-time performance. This work focuses on applying the deep learning-based closed-loop guidance algorithm and error propagation analysis for powered landing, thereby significantly improving the real-time performance. First, a controller consisting of two deep neural networks is constructed to map the thrust direction and magnitude of the rocket according to the state variables. Thereafter, the analytical transition relationships between different uncertainty sources and the state propagation error in a single guidance period are analyzed by adopting linear covariance analysis. Finally, the accuracy of the proposed methods is verified via a comparison with the indirect method and Monte Carlo simulations. Compared with the traditional sequential convex optimization algorithm, our method reduces the computation time from 75 ms to less than 1 ms. Therefore, it shows potential for online applications.