论文标题
指导政策搜索基于高维高级制造过程的控制
Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process
论文作者
论文摘要
在本文中，我们将基于指导的政策搜索（GPS）的加固学习框架用于高维最佳控制问题，该问题在增材制造过程中产生。该问题包括控制过程参数，因此材料的层沉积会导致所得部分表面的所需几何特性，同时最大程度地减少沉积的材料。沉积过程的现实模拟模型以及基于迭代线性二次调节器生成的精心选择的指南分布集用于使用GPS训练神经网络策略。基于训练有素的策略和沉积概况的原位测量的闭环控制经过实验测试，并显示出有希望的性能。
In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that layer-wise deposition of material leads to desired geometric characteristics of the resulting part surface while minimizing the material deposited. A realistic simulation model of the deposition process along with carefully selected set of guiding distributions generated based on iterative Linear Quadratic Regulator is used to train a neural network policy using GPS. A closed loop control based on the trained policy and in-situ measurement of the deposition profile is tested experimentally, and shows promising performance.
