Parametric identification of parallel Wiener–Hammerstein systems

Schoukens, Maarten; Marconato, Anna; Pintelon, R.; Vandersteen, Gerd; Rolain, Yves

doi:10.1016/j.automatica.2014.10.105

Cited by 44 publications

(32 citation statements)

References 34 publications

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“…While the whiteness of the measurement errors are assumed, it can be removed, provided that the measurement errors have finite memory with memory length relatively small compared with the data length. This method can also be used in solving the parameter estimation problem of other nonlinear systems [19,20], such as Hammerstein and Wiener systems [21,22,23], block-oriented nonlinear systems [24,25,26].…”

Section: Discussionmentioning

confidence: 99%

A novel two-stage estimation algorithm for nonlinear Hammerstein–Wiener systems from noisy input and output data

Wang

2017

Journal of the Franklin Institute

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This paper investigates the identification problem of Hammerstein-Wiener errors-invariable systems where the measurement errors of the system input and output are either temporally white or have relatively short memory size compared to the data length, but the corresponding variances are unknown. A two-stage algorithm is developed to estimate the unknown parameters with the first stage employing a modified bias-eliminating least squares algorithm, followed by a singular value decomposition in the second stage. Our proposed estimator is shown to be asymptotically unbiased. The simulation result shows the effectiveness of the proposed algorithm.

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Section: Discussionmentioning

confidence: 99%

A novel two-stage estimation algorithm for nonlinear Hammerstein–Wiener systems from noisy input and output data

Wang

2017

Journal of the Franklin Institute

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“…Under some regularity conditions, the rank of this matrix is equal to the number of branches n, provided that m ≥ n Schoukens et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Structure discrimination in block-oriented models using linear approximations: A theoretic framework

et al. 2015

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In this paper we show that it is possible to retrieve structural information about complex block-oriented nonlinear systems, starting from linear approximations of the nonlinear system around different setpoints. The key idea is to monitor the movements of the poles and zeros of the linearized models and to reduce the number of candidate models on the basis of these observations.Besides the well known open loop single branch Wiener-, Hammerstein-, and Wiener-Hammerstein systems, we also cover a number of more general structures like parallel (multi branch) Wiener-Hammerstein models, and closed loop block oriented models, including linear fractional representation (LFR) models.

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“…It is also being applied in modelling and control design in control engineering field [11]. The advantages of using a block-oriented nonlinear model like H-W are it is simple to understand and easy to use [12]. Thus, it is suitable to apply H-W method to model the head tilt movement behaviour which is nonlinear in nature.…”

Section: Fig 1 -Typical Occupant's Head Tilt Movement During Corneringmentioning

confidence: 99%

The Correlation Model of Head Roll and Lateral Acceleration during Curve Driving via Hammerstein-Wiener

Saruchi¹,

Ariff²,

Zamzuri³

et al. 2019

IJITEE

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Generally, passengers are more prone to Motion Sickness (MS) than the drivers. The difference of their severity level of MS is due to their different head movement towards the direction of the lateral acceleration. During cornering, the passengers tend to tilt their heads according to the direction, while the drivers tends to tilt their head opposite to the direction. Based on this fact, the passengers are able to reduce their MS level if they can imitate the driver’s head movement or lessen their head tilt angle towards the direction of the lateral acceleration. However, it is easier to design MS mitigation method based on the head tilt movement strategy if the mathematical expression of their head behaviour is known beforehand. On way to derive the mathematical expression is by modelling the relationship between the occupant’s head tilt movements and the vehicle’s lateral acceleration during curve driving. Therefore, this study proposed the usage of Hammerstein-Wiener (H-W) method for the modelling purpose. Experiment is set up to obtain the naturalistic data for the modelling process. The modelling process is carried out by varying the input output nonlinearities estimators. The results show that the estimated output responses from the H-W models are similar with the real responses taken from the experiment.The derived models for both passenger and driver have 68.88% and 66.32% of Best Fit (BF) percentages. With further study, the passenger’s and driver’s models which are developed by the proposed H-W modelling strategy are expected to contribute in MS minimisation studies

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Parametric identification of parallel Wiener–Hammerstein systems

Cited by 44 publications

References 34 publications

A novel two-stage estimation algorithm for nonlinear Hammerstein–Wiener systems from noisy input and output data

A novel two-stage estimation algorithm for nonlinear Hammerstein–Wiener systems from noisy input and output data

Structure discrimination in block-oriented models using linear approximations: A theoretic framework

The Correlation Model of Head Roll and Lateral Acceleration during Curve Driving via Hammerstein-Wiener

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