We propose a machine learning enhanced algorithm for solving the optimal landing problem. Using Pontryagin's minimum principle, we derive a two-point boundary value problem for the landing problem. The proposed algorithm uses deep learning to predict the optimal landing time and a space-marching technique to provide good initial guesses for the boundary value problem solver. The performance of the proposed method is studied using the quadrotor example, a reasonably high dimensional and strongly nonlinear system. Drastic improvement in reliability and efficiency is observed.
@article{arxiv.2203.06753,
title = {A Machine Learning Enhanced Algorithm for the Optimal Landing Problem},
author = {Yaohua Zang and Jihao Long and Xuanxi Zhang and Wei Hu and Weinan E and Jiequn Han},
journal= {arXiv preprint arXiv:2203.06753},
year = {2022}
}