Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output-error systems

被引：85
作者：
Kang, Zhen [1 ]
Ji, Yan [1 ]
Liu, Ximei [1 ]
机构：
[1] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266061, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2021年 / 35卷 / 11期
基金：
中国国家自然科学基金;
关键词：
hierarchical identification principle; least squares; nonlinear system; parameter estimation; recursive identification; PARAMETER-ESTIMATION; IDENTIFICATION METHOD; COLLISION-AVOIDANCE; BILINEAR-SYSTEMS; TRACKING CONTROL; FAULT-DIAGNOSIS; STATE; MATRIX;
D O I：
10.1002/acs.3320
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article considers the parameter estimation problem of Hammerstein nonlinear autoregressive output-error systems with autoregressive moving average noises. Applying the key term separation technique, the original system is decomposed into three subsystems: the first subsystem contains the unknown parameters related to the output, the second subsystem contains the unknown parameters related to the input, and the third subsystem contains the unknown parameters related to the noise model. A hierarchical recursive least squares algorithm is proposed based on the hierarchical identification principle for interactively identifying each subsystem. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of Hammerstein nonlinear autoregressive output-error systems.
引用
收藏
页码：2276 / 2295
页数：20
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