论文标题
机器学习增强了最佳着陆问题的算法
A Machine Learning Enhanced Algorithm for the Optimal Landing Problem
论文作者
论文摘要
我们提出了一种机器学习增强的算法，以解决最佳着陆问题。使用Pontryagin的最低原则，我们为着陆问题提出了两点边界价值问题。所提出的算法使用深度学习来预测最佳的着陆时间和空间制定技术，以为边界价值问题解决者提供良好的初始猜测。使用四项示例研究了所提出的方法的性能，该示例是一个相当高的维度和强烈的非线性系统。观察到可靠性和效率的急剧提高。
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.
