论文标题
在有输入非线性的情况下，前馈控制：一种基于学习的方法
Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach
论文作者
论文摘要
先进的前馈控制方法使机甲系统能够以极高的精度和吞吐量执行不同的运动任务。本文的目的是开发一个以数据驱动的前馈控制器来解决输入非线性，这在典型的应用程序（例如半导体后端设备）中很常见。开发的方法由参数逆模型馈电供您组成，该进料供应通过从迭代学习控制中利用想法来进行优化，以跟踪误差降低。模拟设置上的结果表明，对于输入中非线性的系统的现有识别方法的性能提高了。
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.
