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arXiv · 2203.06753

A Machine Learning Enhanced Algorithm for the Optimal Landing Problem

Yaohua Zang·Jihao Long·Xuanxi Zhang·Wei Hu·Weinan E·Jiequn Han
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Abstract

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.

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Yaohua Zang, Jihao Long, Xuanxi Zhang, Wei Hu, Weinan E, Jiequn Han. 2022-03-13. A Machine Learning Enhanced Algorithm for the Optimal Landing Problem. https://arxiv.org/abs/2203.06753

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