论文标题
关于模型在学习控制中的作用：参与者 - 批判性迭代学习控制
On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control
论文作者
论文摘要
从过去任务的数据中学习可以基本上提高机电系统系统的准确性。通常，要快速安全地学习系统模型。本文的目的是开发一种无模型的方法，用于用于机电系统系统的快速安全学习。开发的Actor-Critic迭代学习控制（ACILC）框架使用了带有基础功能的前馈参数化。这些基础函数编码隐式模型知识和参与者批评算法可以在不明确使用模型的情况下学习前馈参数。打印机设置的实验结果表明，开发的ACILC框架能够在不使用明确的模型知识的情况下实现与基于模型的方法相同的前馈信号。
Learning from data of past tasks can substantially improve the accuracy of mechatronic systems. Often, for fast and safe learning a model of the system is required. The aim of this paper is to develop a model-free approach for fast and safe learning for mechatronic systems. The developed actor-critic iterative learning control (ACILC) framework uses a feedforward parameterization with basis functions. These basis functions encode implicit model knowledge and the actor-critic algorithm learns the feedforward parameters without explicitly using a model. Experimental results on a printer setup demonstrate that the developed ACILC framework is capable of achieving the same feedforward signal as preexisting model-based methods without using explicit model knowledge.
