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 …

We consider the Earth–Venus mass-optimal interplanetary transfer of a low-thrust spacecraft
and show that the optimal guidance can be represented by deep networks in a large portion …

Computing optimal feedback controls for nonlinear systems generally requires solving
Hamilton--Jacobi--Bellman (HJB) equations, which are notoriously difficult when the state …

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

Recent research reveals that deep learning is an effective way of solving high dimensional
Hamilton–Jacobi–Bellman equations. The resulting feedback control law in the form of a …

In this letter we propose a new computational method for designing optimal regulators for
high-dimensional nonlinear systems. The proposed approach leverages physics-informed …

In this paper, neural networks are trained to learn the optimal time, the initial costates, and
the optimal control law of time-optimal low-thrust interplanetary trajectories. The aim is to …

One major capability of a Deep Reinforcement Learning (DRL) agent to control a specific
vehicle in an environment without any prior knowledge is decision-making based on a well …

Recent research shows that supervised learning can be an effective tool for designing near-
optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the …

The development of robust fuel-optimal feedback controllers for the pinpoint landing
problem has application to a variety of aerospace vehicles including lunar and planetary …