An Identification Based Indirect Iterative Learning Control via Data-driven Approach

被引：0

作者：

Chi, Ronghu ^{[1
]}

Su, Tao ^{[1
]}

Jin, Shangtai ^{[2
]}

机构：

[1] Qingdao Univ Sci & Technol, Sch Automat & Elect Engn, Qingdao, Peoples R China

[2] Beijing Jiaotong Univ, Adv Control Syst Lab, Beijing, Peoples R China

来源：

2012 12TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS & VISION (ICARCV) | 2012年

基金：

美国国家科学基金会;

关键词：

iterative learning control; nonlinear discrete system; linearized model; iterative identification; convergence; UNCERTAIN ROBOTIC SYSTEMS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an iterative learning control approach is developed for a class of uncertainty nonlinear discrete-time systems based on the identification of the controlled system. At first, the linearized model of the nonlinear system is proposed. And then using the identification method, we present an indirect iterative learning control scheme for the controlled system. Analysis shows that the scheme can guarantee the system convergence under some conditions.

引用

页码：1773 / 1776

页数：4

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