论文标题
一个针对受限轨迹的端到端数据驱动的优化框架
An end-to-end data-driven optimisation framework for constrained trajectories
论文作者
论文摘要
许多现实世界中的问题需要在约束下优化轨迹。经典方法基于最佳控制方法，但需要对基本动力学的确切了解，这可能是具有挑战性甚至无法触及的。在本文中，我们利用数据驱动的方法来设计一种新的端到端框架，该框架无动态，用于优化和现实的轨迹。我们首先以功能为基础分解轨迹，在多元功能空间上交易初始无限尺寸问题，以进行参数优化问题。最大\ emph {A后验}方法包含来自数据的信息，用于获得正规化的新优化问题。刑罚术语将搜索重点放在以数据为中心的区域上，并在问题中包括估计的线性约束。我们将数据驱动的方法应用于航空和帆路线优化的两种设置，从而产生指挥结果。开发的方法已在Python Library Perotor中实现。
Many real-world problems require to optimise trajectories under constraints. Classical approaches are based on optimal control methods but require an exact knowledge of the underlying dynamics, which could be challenging or even out of reach. In this paper, we leverage data-driven approaches to design a new end-to-end framework which is dynamics-free for optimised and realistic trajectories. We first decompose the trajectories on function basis, trading the initial infinite dimension problem on a multivariate functional space for a parameter optimisation problem. A maximum \emph{a posteriori} approach which incorporates information from data is used to obtain a new optimisation problem which is regularised. The penalised term focuses the search on a region centered on data and includes estimated linear constraints in the problem. We apply our data-driven approach to two settings in aeronautics and sailing routes optimisation, yielding commanding results. The developed approach has been implemented in the Python library PyRotor.
