迭代学习控制
控制理论(社会学)
计算机科学
代表(政治)
参数统计
加速度
控制器(灌溉)
LTI系统理论
线性系统
趋同(经济学)
背景(考古学)
系统动力学
控制(管理)
控制工程
数学
人工智能
工程类
数学分析
农学
古生物学
统计
物理
经典力学
政治
政治学
法学
经济
生物
经济增长
作者
Jia Wang,Leander Hemelhof,Ivan Markovsky,Panagiotis Patrinos
出处
期刊:Cornell University - arXiv
日期:2023-01-01
标识
DOI:10.48550/arxiv.2312.14326
摘要
This paper studies data-driven iterative learning control (ILC) for linear time-invariant (LTI) systems with unknown dynamics, output disturbances and input box-constraints. Our main contributions are: 1) using a non-parametric data-driven representation of the system dynamics, for dealing with the unknown system dynamics in the context of ILC, 2) design of a fast ILC method for dealing with output disturbances, model uncertainty and input constraints. A complete design method is given in this paper, which consists of the data-driven representation, controller formulation, acceleration strategy and convergence analysis. A batch of numerical experiments and a case study on a high-precision robotic motion system are given in the end to show the effectiveness of the proposed method.
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