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

This paper deals with the optimization of the ascent trajectory of a multistage launch vehicle, from liftoff to the payload injection into the target orbit, considering inverse-square gravity accel...

This paper outlines a novel approach to the design of optimal space trajectories under significant uncertainty. Finite-horizon covariance control, i.e., the steering of a system from an initial pro...

This paper presents a convex programming approach to the optimization of a multistage launch vehicle ascent trajectory, from the liftoff to the payload injection into the target orbit, taking into ...

This paper focuses on the use of meta-reinforcement learning for the autonomous guidance of a spacecraft during the terminal phase of an impact mission toward a binary asteroid system. The control ...

Solving constrained optimal control problems (OCPs) is essential to ensure safety in real-world scenarios. Recent machine learning techniques have shown promise in addressing OCPs. This paper introduces a novel methodology for solving OCPs with path constraints using physics-informed neural networks. Specifically, Pontryagin neural networks (PoNNs), which solve the boundary value problem arising from the indirect method and Pontryagin minimum principle, are extended to handle path constraints. In this new formulation, path constraints are incorporated into the Hamiltonian through additional Lagrange multipliers, which are treated as optimization variables. The complementary slackness conditions are enforced by ensuring the zero value of the Fischer–Burmeister function within the loss functions to be minimized. This approach adds minimal complexity to the original PoNN framework, as it avoids the need for continuation methods, penalty functions, or additional differential equations, which are often required in traditional methods to solve path-constrained OCPs via the indirect method. Numerical results for a fixed-time energy-optimal rendezvous with various path constraints and a constrained optimal rocket ascent demonstrate the effectiveness of the proposed method in solving path-constrained OCPs.