Continuous-Time Model Identification From Filtered Sampled Data: Error Analysis

被引：5
|
作者：
Hu, Xiao-Li [1 ,2 ]
Welsh, James S. [3 ]
机构：
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
[2] Sun Yat Sen Univ, Nanfang Coll, Sch Elect & Comp Engn, Guangzhou 510275, Peoples R China
[3] Univ Newcastle, Sch Elect Engn & Comp, Newcastle, NSW 2308, Australia
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2020年 / 65卷 / 10期
关键词：
Mathematical model; Data models; Estimation; Analytical models; Upper bound; Continuous time systems; Error analysis; Continuous-time model; system identification; sampled data; SYSTEM-IDENTIFICATION;
D O I：
10.1109/TAC.2020.3006354
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 article, an upper bound is established for the estimation error of a standard least squares (LS) algorithm used to identify a continuous-time model from filtered, sampled input-output data. It is found that the error has three constituent components due to the initial conditions, observation noise, and sample period. In particular, the initial condition bias is bounded by O(1/[N Delta t]), which requires sufficiently large [N Delta t] for accurate LS estimation. The theoretical results obtained are confirmed by simulation.
引用
收藏
页码：4005 / 4015
页数：11
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