Abstract Space mission design places a premium on cost and operational efficiency. The
search for new science and life beyond Earth calls for spacecraft that can deliver scientific …

This paper investigates the use of reinforcement learning for the robust design of low-thrust
interplanetary trajectories in presence of severe uncertainties and disturbances, alternately …

This paper investigates the use of deep learning techniques for real-time optimal spacecraft
guidance during terminal rendezvous maneuvers, in presence of both operational …

In this article, an intelligent predictor-corrector entry guidance approach for lifting hypersonic
vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the …

Recent development of deep learning has shown that a deep neural network (DNN) is
capable of learning the underlying nonlinear relationship between the state and the optimal …

This paper presents a novel framework, combining the indirect method and Physics-
Informed Neural Networks (PINNs), to learn optimal control actions for a series of optimal …

In this study, an adaptive neural network control approach is proposed to achieve accurate
and robust control of nonlinear systems with unknown dynamics, wherein the neural network …

This paper focuses on improving the autonomy and efficiency of fuel-optimal powered
landing guidance for reusable rockets considering aerodynamic forces. Deep-learning …

This paper investigates the trajectory design for landing on a small celestial body with a
flexible lander. The flexible lander features a flexible structure that increases surface contact …

Autonomy is an increasingly essential component of future space missions, and new
technologies are necessary to accommodate off-nominal occurrences onboard that may …