Fuel-optimal powered descent guidance for lunar pinpoint landing using neural networks

This paper presents a Neural Networks (NNs) based approach to generate the fuel-optimal powered descent guidance for lunar pinpoint landing. According to Pontryagin's Minimum Principle, the optimality conditions are first derived. To generate the dataset of optimal trajectories for training NNs, we formulate a parameterized system, which allows for generating each optimal trajectory by a simple propagation without using any optimization method. Then, a dataset containing the optimal state-thrust vector pairs can be readily collected. Since it is challenging for NNs to approximate a bang-bang (or discontinuous) type of optimal thrust magnitude, we introduce a regularisation function to the switching function so that the regularized switching function approximated by a simple NN can be used to represent the optimal thrust magnitude. Meanwhile, another two well-trained NNs are used to predict the thrust steering angle and the time of flight given a flight state. Finally, numerical simulations show that the proposed method is capable of generating the optimal guidance that steers the lunar lander to the desired landing site with acceptable landing errors. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.