Identification of Wiener–Hammerstein models: Two algorithms based on the best split of a linear model applied to the SYSID'09 benchmark problem

Sjöberg, Jonas; Lauwers, Lieve; Schoukens, Joannes

doi:10.1016/j.conengprac.2012.07.001

Cited by 29 publications

(17 citation statements)

References 14 publications

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“…They will not be detected during the parametrization of the BLA. This assumption is quite common for Wiener-Hammerstein identification algorithms, see for example the two identification algorithms presented in [28]. However, there exist different Wiener-Hammerstein and S M identification algorithms that do not need this assumption [2,5,24,33].…”

Section: Definition 2 Best Linear Approximation (Bla)mentioning

confidence: 99%

Parametric identification of parallel Wiener–Hammerstein systems

Schoukens¹,

Marconato²,

Pintelon³

et al. 2015

Automatica

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a b s t r a c tBlock-oriented nonlinear models are popular in nonlinear modeling because of their advantages to be quite simple to understand and easy to use. To increase the flexibility of single branch block-oriented models, such as Hammerstein, Wiener, and Wiener-Hammerstein models, parallel block-oriented models can be considered. This paper presents a method to identify parallel Wiener-Hammerstein systems starting from input-output data only. In the first step, the best linear approximation is estimated for different input excitation levels. In the second step, the dynamics are decomposed over a number of parallel orthogonal branches. Next, the dynamics of each branch are partitioned into a linear time invariant subsystem at the input and a linear time invariant subsystem at the output. This is repeated for each branch of the model. The static nonlinear part of the model is also estimated during this step. The consistency of the proposed initialization procedure is proven. The method is validated on real-world measurements using a custom built parallel Wiener-Hammerstein test system.

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Section: Definition 2 Best Linear Approximation (Bla)mentioning

confidence: 99%

Parametric identification of parallel Wiener–Hammerstein systems

Schoukens¹,

Marconato²,

Pintelon³

et al. 2015

Automatica

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“…Several methods have been proposed to make this split. Here we briefly discuss three of them, namely the bruteforce and the advanced method in Sjöberg et al [2012], and the QBLA method in Westwick and Schoukens [2012].…”

Section: Related Initialization Methods For Wiener-hammerstein Systemsmentioning

confidence: 99%

“…The brute-force method in Sjöberg et al [2012] scans all possible splits. For each of these splits, the static nonlinearity is estimated via a linear least-squares regression.…”

Section: Related Initialization Methods For Wiener-hammerstein Systemsmentioning

confidence: 99%

Generation of initial estimates for Wiener-Hammerstein models via basis function expansions

Tiels

Schoukens

2014

IFAC Proceedings Volumes

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“…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.…”

Section: Introductionmentioning

confidence: 99%