Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique

Tan, Ai Hui; Wong, Hin Kwan; Godfrey, K.R.

doi:10.1016/j.conengprac.2012.04.007

Cited by 8 publications

(9 citation statements)

References 12 publications

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“…In ; Tan et al (2012), both the poles and zeros of the best transfer function are split in all possible ways between two linear subsystems to get the initial estimates for subsequent nonlinear optimizations. In the proposed state space method, only the poles are split between two linear subsystems and the parameters b 1 and c 2 are estimated by using the separable least-squares.…”

Section: Discussionmentioning

confidence: 99%

“…But, the benchmark session at SYSID 2009 (Schoukens, Suykens, & Ljung, 2008) has stimulated the interest in identification of Wiener-Hammerstein systems; see the Special Section in Control Engineering Practice (CEP) with nine papers (Wills & Ninness, 2012;Piroddi, Farina, & Lovera, 2012;Sjöberg, Lauwers, & Schoukens, 2012;Marconato, Sjöberg, & Schoukens, 2012;Paduart, Lauwers, Pintelon, & Schoukens, 2012;Tan, Wong, & Godfrey, 2012;Han & de Callafon, 2012;Lopes dos Santos, Ramos, & de Carvalho, 2012;Falck et al, 2012). Among others, in , Tan et al (2012), the transfer function of the best linear approximation of the Wiener-Hammerstein system is first obtained by least-squares or frequency domain methods. Then, the transfer function is divided into two subsystems to get the estimates for initialization and identification.…”

Section: Introductionmentioning

confidence: 99%

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A subspace-based identification of Wiener–Hammerstein benchmark model

Ase

Katayama

2015

Control Engineering Practice

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A subspace-based identification of Wiener–Hammerstein benchmark model

Ase

Katayama

2015

Control Engineering Practice

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“…Many approaches use the BLA, or a similar correlation analysis, as a starting point for the algorithm e.g. (Billings & Fakhouri, 1978a;Billings, 1980;Westwick & Kearney, 2003;Crama & Schoukens, 2005;Lauwers, 2011;Tan et al, 2012;Westwick & Schoukens, 2012;Schoukens et al, 2014a;Vanbeylen, 2014;Tiels et al, , 2015Giordano & Sjöberg, 2015;Ase & Katayama, 2015;Katayama & Ase, 2016).…”

Section: Wiener-hammerstein Structurementioning

confidence: 99%

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

2017

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Block-oriented nonlinear models are popular in nonlinear system identification because of their advantages of being simple to understand and easy to use. Many different identification approaches were developed over the years to estimate the parameters of a wide range of block-oriented nonlinear models. One class of these approaches uses linear approximations to initialize the identification algorithm. The best linear approximation framework and the -approximation framework, or equivalent frameworks, allow the user to extract important information about the system, guide the user in selecting good candidate model structures and orders, and prove to be a good starting point for nonlinear system identification algorithms. This paper gives an overview of the different block-oriented nonlinear models that can be identified using linear approximations, and of the identification algorithms that have been developed in the past. A non-exhaustive overview of the most important other block-oriented nonlinear system identification approaches is also provided throughout this paper.

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“…Method/technique RMS (mV) Nonparametric BLA, QBLA [41] 0.278 44 Classification of poles and zeros using QBLA [42] 0.286 26 Fractional model parameterization [43] 0.295 26 Advanced method [44,45] 0.30 64 WH-EA (this paper) 0.306 26 Brute force method [45] 0.31 30 Scanning technique [46] 0.370 -Polynomial nonlinear state space [47] 0.42 797 Generalized Hammerstein-Wiener [48] 0.481 47 Incremental nonlinear optimization [49] 0.679 25 LS-SVMs [50] 4.070 -Biosocial culture [51] 8 Figure 16: Captured nonlinearity as a piecewise linear function with = 8 by WH-EA from the benchmark data. Notice that the nonlinear block characterization only needs 14 parameters since the first and last straight segments can be defined just with their angles.…”

Section: Wh-ea Parametersmentioning

confidence: 99%

WH‐EA: An Evolutionary Algorithm for Wiener‐Hammerstein System Identification

et al. 2018

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Current methods to identify Wiener-Hammerstein systems using Best Linear Approximation (BLA) involve at least two steps. First, BLA is divided into obtaining front and back linear dynamics of the Wiener-Hammerstein model. Second, a refitting procedure of all parameters is carried out to reduce modelling errors. In this paper, a novel approach to identify Wiener-Hammerstein systems in a single step is proposed. This approach is based on a customized evolutionary algorithm (WH-EA) able to look for the best BLA split, capturing at the same time the process static nonlinearity with high precision. Furthermore, to correct possible errors in BLA estimation, the locations of poles and zeros are subtly modified within an adequate search space to allow a fine-tuning of the model. The performance of the proposed approach is analysed by using a demonstration example and a nonlinear system identification benchmark.

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Identification of a Wiener–Hammerstein system using an incremental nonlinear optimisation technique

Cited by 8 publications

References 12 publications

A subspace-based identification of Wiener–Hammerstein benchmark model

A subspace-based identification of Wiener–Hammerstein benchmark model

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

WH‐EA: An Evolutionary Algorithm for Wiener‐Hammerstein System Identification

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