Auxiliary model-based least-squares identification methods for Hammerstein output-error systems

被引：235
|
作者：
Ding, Feng [1 ]
Shi, Yang
Chen, Tongwen
机构：
[1] So Yangtze Univ, Control Sci & Engn Res Ctr, Wuxi 214122, Peoples R China
[2] Univ Saskatchewan, Dept Engn Mech, Saskatoon, SK S7N 5A9, Canada
[3] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2V4, Canada
来源：
SYSTEMS & CONTROL LETTERS | 2007年 / 56卷 / 05期
基金：
中国国家自然科学基金; 加拿大自然科学与工程研究理事会;
关键词：
recursive identification; parameter estimation; least squares; multi-innovation identification; hierarchical identification; auxiliary model; convergence properties; stochastic gradient; Hammerstein models; Wiener models; Martingale convergence theorem;
D O I：
10.1016/j.sysconle.2006.10.026
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The difficulty in identification of a Hammerstein (a linear dynamical block following a memoryless nonlinear block) nonlinear output-error model is that the information vector in the identification model contains unknown variables-the noise-free (true) outputs of the system. In this paper, an auxiliary model-based least-squares identification algorithm is developed. The basic idea is to replace the unknown variables by the output of an auxiliary model. Convergence analysis of the algorithm indicates that the parameter estimation error consistently converges to zero under a generalized persistent excitation condition. The simulation results show the effectiveness of the proposed algorithms. (C) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：373 / 380
页数：8
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