Frequency Domain Identification of Hammerstein Systems

被引:0
|
作者
Swain, Akshya K. [1 ]
Westwick, David T. [2 ]
Perreault, Eric J. [3 ]
机构
[1] Univ Auckland, Dept Elect & Comp Engn, Auckland 1, New Zealand
[2] Univ Calgary, Dept Elect & Comp Engn, Calgary, AB, Canada
[3] Northwestern Univ, Dept Biomed Engn, N Chicago, IL 60064 USA
关键词
RECURSIVE-IDENTIFICATION; MODELS; CONVERGENCE;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The present study proposes a new approach to identify the parameters of both continuous and discrete time Hammerstein systems in frequency domain. A harmonic probing technique is used to derive the linear and higher-order frequency response functions (called the generalized frequency response functions (GFRF)) of both discrete and continuous-time Hammerstein models. The computation of the n-th order generalized frequency response functions (GFRF) is a recursive procedure where each lower order GFRF contains no effects from higher order terms. Thus the parameter estimation problem can be formulated in a linear least squares framework where the parameters corresponding to nonlinearities of different orders can be estimated independently, beginning with first order and then building up to include the nonlinear terms using the weighted complex orthogonal estimator, which is a modified version of the standard orthogonal least squares, to accommodate complex data. Simulation results are included to demonstrate that the proposed method can successfully estimate the parameters of the system under the effects of significant levels of noise.
引用
收藏
页码:1216 / +
页数:2
相关论文
共 50 条
  • [31] Recursive Identification for MIMO Hammerstein Systems
    Chen Xing-Min
    Chen Han-Fu
    PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE, 2010, : 6215 - 6220
  • [32] RECURSIVE NONPARAMETRIC IDENTIFICATION OF HAMMERSTEIN SYSTEMS
    GREBLICKI, W
    PAWLAK, M
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 1989, 326 (04): : 461 - 481
  • [33] IDENTIFICATION OF HAMMERSTEIN SYSTEMS WITH QUANTIZED OBSERVATIONS
    Zhao, Yanlong
    Zhang, Ji-Feng
    Wang, Le Yi
    Yin, G. George
    SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 2010, 48 (07) : 4352 - 4376
  • [34] Recursive Identification for MIMO Hammerstein Systems
    Chen, Xing-Min
    Chen, Han-Fu
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2011, 56 (04) : 895 - 902
  • [35] Recursive nonparametric identification of Hammerstein systems
    Greblicki, Wlodzimierz, 1600, (326):
  • [36] Identification of Hammerstein Nonlinear Stochastic Systems
    G. R. Bolkvadze
    Automation and Remote Control, 2002, 63 : 601 - 612
  • [37] Parametric Identification of Parallel Hammerstein Systems
    Schoukens, Maarten
    Pintelon, Rik
    Rolain, Yves
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2011, 60 (12) : 3931 - 3938
  • [38] Identification methods for Hammerstein nonlinear systems
    Ding, Feng
    Liu, Xiaoping Peter
    Liu, Guangjun
    DIGITAL SIGNAL PROCESSING, 2011, 21 (02) : 215 - 238
  • [39] Identification of Hammerstein systems with continuous nonlinearity
    Chen, Jing
    Wang, Xiuping
    INFORMATION PROCESSING LETTERS, 2015, 115 (11) : 822 - 827
  • [40] Uniquely connecting frequency domain representations of given order polynomial Wiener-Hammerstein systems
    Rijlaarsdam, David
    Oomen, Tom
    Nuij, Pieter
    Schoukens, Johan
    Steinbuch, Maarten
    AUTOMATICA, 2012, 48 (09) : 2381 - 2384