Abstract

This paper investigates the three-dimensional (3D) multi-point landing guidance (MLG) problem with hazard avoidance by developing a mixed-input learning-based method to achieve precise and fuel-efficient planetary landing in future Mars missions. Specifically, we aim to find a safe, fuel-efficient landing point and generate a fuel-optimal trajectory simultaneously in real-time. First, by introducing binary variables, the MLG problem is formulated as an optimal control problem with quadratic constraints. Then, by formulating the Hamiltonian function, the necessary conditions of optimality for the MLG problem are obtained, where the critical parameters are identified to represent the complete optimal solution. After that, to find the implicit relationship between the problem inputs and these critical parameters, a hybrid deep neural network is constructed. To be specific, on the one hand, the contour maps of the landing area, which are image inputs, are adopted to reflect the features of the pre-defined landing area. On the other hand, the velocity and position vectors, which belong to numeric inputs, are adopted to reflect the features of the initial state of the powered descent phase. Finally, with the constructed hybrid deep neural network well trained, the mixed-input learning-based optimal control solution can be computed onboard. To verify the effectiveness and accuracy of the proposed method, the simulation results of 3D MLG problems are presented and analyzed.

Department(s)

Mechanical and Aerospace Engineering

Publication Status

Full Access

International Standard Book Number (ISBN)

978-162410699-6

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 American Institute of Aeronautics and Astronautics, All rights reserved.

Publication Date

01 Jan 2023

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