This paper presents a convex approach to the numerical solution of the minimum-fuel low-thrust orbit transfer problem. The main contribution is the transformation of the original nonlinear optimal control problem into a sequence of convex optimization problems. First, the control is decoupled from the states through a change of variables. Then, by introducing a lossless convexification technique, the control constraints are convexified, and the original problem is relaxed into a sequence of second-order cone programming problems. The resulting subproblems can be solved in real time by efficient interior-point methods. Finally, the effectiveness of the proposed methodology is demonstrated through numerical simulations of the three-dimensional minimum-fuel Earth-to-Mars low-thrust transfer problem.

In this study, 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 deep neural network (DNN) and constraint management techniques. First, the entry trajectory planning problem is formulated as a univariate root-finding problem based on a compound bank angle corridor, and two constraint management algorithms are presented to enforce the satisfaction of both path and terminal constraints. Second, a DNN is developed to learn the mapping relationship between the flight states and ranges, and experiments are conducted to verify its high approximation accuracy. Based on the DNN-based range predictor, an intelligent, multi-constrained predictor-corrector guidance algorithm is developed to achieve real-time trajectory correction and lateral heading control with a determined number of bank reversals. Simulations are conducted through comparing with the state-of-the-art predictor-corrector algorithms, and the results demonstrate that the proposed DNNbased entry guidance can achieve the trajectory correction with an update frequency of 20 Hz and is capable of providing highprecision, safe, and robust entry guidance for hypersonic vehicles.

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...

To improve the autonomy and reliability of asteroid landings, an intelligent approach to fast and reliable generation of optimal landing trajectories is proposed. Unfortunately, the indirect methods require good initial guesses for shootings. To address this issue, deep neural networks (DNNs) are developed to approximate the costates of the indirect methods and supply good initial solutions to achieve high success rate of shootings. This article focuses on the following three contributions. First, the original asteroid landing problems are connected with two-dimensional (2-D) asteroid-free transfer problems using model continuation and state transformation techniques. Second, DNNs are developed and optimized to approximate the costates of the 2-D transfers with high accuracy. Third, a systematical solution for asteroid landing trajectory optimization is developed, wherein the DNNs provide good initial solutions, and the accurate solutions for landings are obtained through a solution continuation process. Additionally, an alternative solution supplying strategy based on error statistics of DNN approximation is presented to take over the solution supplying when the predictions of DNNs fail to converge to the optima. Evaluations of the DNNs for solution supplying and simulations of landings on 433 Eros and 101955 Bennu are given to substantiate the effectiveness of the proposed techniques and demonstrate the performance of the developed algorithm.