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.

中文翻译：

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模型在学习控制中的作用：Actor-Critic 迭代学习控制

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，为了快速安全地学习，需要系统模型。本文的目的是开发一种无模型方法，用于机电系统的快速安全学习。开发的actor-critic迭代学习控制（ACILC）框架使用具有基函数的前馈参数化。这些基函数编码隐式模型知识，actor-critic 算法在不显式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 框架能够在不使用显式模型知识的情况下实现与先前存在的基于模型的方法相同的前馈信号。