Advances in trajectory optimization for space vehicle control
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 …
search for new science and life beyond Earth calls for spacecraft that can deliver scientific …
Reinforcement learning for robust trajectory design of interplanetary missions
A Zavoli, L Federici - Journal of Guidance, Control, and Dynamics, 2021 - arc.aiaa.org
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 …
interplanetary trajectories in presence of severe uncertainties and disturbances, alternately …
Deep learning techniques for autonomous spacecraft guidance during proximity operations
This paper investigates the use of deep learning techniques for real-time optimal spacecraft
guidance during terminal rendezvous maneuvers, in presence of both operational …
guidance during terminal rendezvous maneuvers, in presence of both operational …
Multiconstrained real-time entry guidance using deep neural networks
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 …
vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the …
Onboard generation of optimal trajectories for hypersonic vehicles using deep learning
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 …
capable of learning the underlying nonlinear relationship between the state and the optimal …
Physics-informed neural networks for optimal planar orbit transfers
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 …
Informed Neural Networks (PINNs), to learn optimal control actions for a series of optimal …
Adaptive neural network control of nonlinear systems with unknown dynamics
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 …
and robust control of nonlinear systems with unknown dynamics, wherein the neural network …
Real-time guidance for powered landing of reusable rockets via deep learning
J Wang, H Ma, H Li, H Chen - Neural Computing and Applications, 2023 - Springer
This paper focuses on improving the autonomy and efficiency of fuel-optimal powered
landing guidance for reusable rockets considering aerodynamic forces. Deep-learning …
landing guidance for reusable rockets considering aerodynamic forces. Deep-learning …
Trajectory design for landing on small celestial body with flexible lander
Z Chen, J Long, P Cui - Acta Astronautica, 2023 - Elsevier
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 …
flexible lander. The flexible lander features a flexible structure that increases surface contact …
Adaptive closed-loop maneuver planning for low-thrust spacecraft using reinforcement learning
NB LaFarge, KC Howell, DC Folta - Acta Astronautica, 2023 - Elsevier
Autonomy is an increasingly essential component of future space missions, and new
technologies are necessary to accommodate off-nominal occurrences onboard that may …
technologies are necessary to accommodate off-nominal occurrences onboard that may …