Six-DOF Spacecraft Optimal Trajectory Planning and Real-Time Attitude Control: A Deep Neural Network-Based Approach

TLDR

The study proposes an integrated 6‑DOF hypersonic vehicle reentry framework that jointly plans trajectories and controls attitude. A bilevel approach is used, where an upper level generates optimal state‑control trajectories and a lower level trains deep neural networks on this data to produce real‑time feedback actions, with simulations confirming its applicability and optimality compared to other optimization‑based controllers. Simulation results demonstrate that the bilevel DNN‑driven controller reliably controls hypersonic reentry in real time.

Abstract

This brief presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory data set containing optimal system control and state trajectories is generated, while in the lower level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, a comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time.