Iterative Learning Control Design with High-Order Internal Model for Nonlinear Systems

被引：8
|
作者：
Yin, Chenkun [1 ]
Xu, Jian-Xin [2 ]
Hou, Zhongsheng [1 ]
机构：
[1] Beijing Jiaotong Univ, Adv Control Syst Lab, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
[2] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
来源：
PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009) | 2009年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1109/CDC.2009.5399801
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 work we focus on iterative learning control (ILC) design for tracking iteration-varying reference trajectories that are generated by high-order internal models (HOIM). An HOIM can be formulated as a polynomial operator between consecutive iterations to describe the changes of desired trajectories in the iteration domain. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a unity coefficient or a special first order internal model. By inserting the HOIM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Utilizing of conventional time-weighted norm method guarantees validity of proposed algorithm in a sense of data-driven control.
引用
收藏
页码：434 / 439
页数：6
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