论模型在学习控制中的作用：演员-评论家迭代学习控制
On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control
摘要：
从过去任务的数据中学习可以大大提高机电系统的准确性。通常，为了快速和安全地学习，需要系统的模型。本文的目的是开发一种无模型的方法，用于机电一体化系统的快速和安全的学习。所开发的演员-评论家迭代学习控制（ACILC）框架使用了前馈参数化与基函数。这些基函数对隐式模型知识进行编码，并且actor-critic算法在不显式使用模型的情况下学习前馈参数。实验结果表明，开发的ACILC框架是能够实现相同的前馈信号预先存在的基于模型的方法，而不使用显式的模型知识。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
