Iterative Learning Identification for Discrete Parabolic Distributed Parameter Systems

被引：0
|
作者：
Liu, Lanlan [2 ]
Dai, Xisheng [1 ,2 ]
Zhou, Xingyu [2 ]
Yu, Shali [3 ]
机构：
[1] Guangxi Higher Educ Inst, Key Lab Ind Proc Intelligent Control Technol, Liuzhou 545006, Peoples R China
[2] Guangxi Univ Sci & Technol, Sch Elect & Informat Engn, Liuzhou 545006, Peoples R China
[3] South China Univ Technol, Guangzhou Coll, Guangzhou 510640, Guangdong, Peoples R China
来源：
PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS) | 2018年
基金：
中国国家自然科学基金;
关键词：
Iterative learning identification; discrete distributed parameter systems; learning convergence;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper presents an iterative learning identification scheme for discrete parabolic distributed parameter systems with unknown curve surface parameters. The method achieves identification through iterative learning control concepts, a P-type learning identification controller is employed to estimate the spatial-temporal varying curve surface iteratively. Then, the sufficient convergence conditions for identification error in the sense of L-2 norm has been presented through rigorous analysis. In the end, numerical simulations are shown to illustrate the effectiveness of the proposed learning identification algorithm.
引用
收藏
页码：588 / 592
页数：5
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