A recursive hierarchical parametric estimation algorithm for nonlinear systems described by Wiener‐Hammerstein models

Ghanmi, Afef; Elloumi, Mourad; Salhi, Houda; Kamoun, S.

doi:10.1002/asjc.2029

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…There are a number of parameter estimation methods for the Hammerstein model. Generally, they are mainly divided into two categories, that is, synchronous parameter estimation method [9][10][11][12][13][14] and asynchronous parameter estimation method [15][16][17][18][19][20][21][22][23][24]. The synchronous parameter estimation methods do not need to estimate the intermediate unmeasurable variables of the model, and the parameter estimation values are calculated directly by using mixed parameters model of the nonlinear block and the linear block, for instance, subspace method [9][10][11], direct identification algorithm [12], and over-parameter method [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Generally, they are mainly divided into two categories, that is, synchronous parameter estimation method [9][10][11][12][13][14] and asynchronous parameter estimation method [15][16][17][18][19][20][21][22][23][24]. The synchronous parameter estimation methods do not need to estimate the intermediate unmeasurable variables of the model, and the parameter estimation values are calculated directly by using mixed parameters model of the nonlinear block and the linear block, for instance, subspace method [9][10][11], direct identification algorithm [12], and over-parameter method [13,14]. In contrast, asynchronous parameter estimation methods are carried out for estimating separately the nonlinear block parameters and the linear block parameters through reconstructing unmeasurable variables, such as special signal methods [15,16], iterative methods [18,19], frequency domain methods [20][21][22], random algorithms [23], and separable least square methods [24].…”

Section: Introductionmentioning

confidence: 99%

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Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Zhu

et al. 2022

Asian Journal of Control

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A novel parameter learning scheme using multi-signal processing is developed that aims at estimating parameters of the Hammerstein nonlinear model with output disturbance in this paper. The Hammerstein nonlinear model consists of a static nonlinear block and a dynamic linear block, and the multi-signals are devised to estimate separately the nonlinear block parameters and the linear block parameters; the parameter estimation procedure is greatly simplified. Firstly, in view of the input-output data of separable signals, the linear block parameters are computed through correlation analysis method, thereby the influence of output noise is effectively handled. In addition, model error probability density function technology is employed to estimate the nonlinear block parameters with the help of measurable input-output data of random signals, which not only controls the space state distribution of model error but also makes error distribution tends to normal distribution. The simulation results demonstrate that the developed approach obtains high learning accuracy and small modeling error, which verifies the effectiveness of the developed approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Zhu

et al. 2022

Asian Journal of Control

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“…In Miranda-Colorado and Moreno-Valenzuela [3], a method for online parameter identification applied to second-order nonlinear servomechanisms was presented. Also, Ghanmi et al [4] proposed a recursive hierarchical parametric estimation (RHPE) for stochastic nonlinear systems described by Wiener-Hammerstein (W-H) mathematical models, and Beltran-Carbajal and Silva-Navarro [5] was presented a new algebraic parametric identification method in the time domain that estimates the parameters of multiple degrees-of-freedom mechanical vibrating systems. On the other hand, some researchers have proposed various research methods to detect failures in nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

A study on the state and parameter cooperative estimation of nonlinear mechanical systems

Pak

Choi

Han

2021

Asian Journal of Control

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“…In this paper we consider a design of a robust recursive identification algorithm. The problem belongs to the robust identification of block-oriented nonlinear models [9,10]. The nonlinear model has a structure of a NARMAX model.…”

Section: Introductionmentioning

confidence: 99%

Robust Recursive Identification of Hammerstein Models Based on Weiszfald Algorithm

Filipović

2020

FU Aut Cont Rob

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The Hammerstein models can accurately describe a wide variety of nonlinear systems (chemical process, power electronics, electrical drives, sticky control valves). Algorithms of identification depend, among other, on the assumption about the nature of stochastic disturbance. Practical research shows that disturbances, owing the presence of outliers, have a non-Gaussian distribution. In such case it is a common practice to use the robust statistics. In the paper, by analysis of the least favourable probability density, it is shown that the robust (Huber`s) estimation criterion can be presented as a sum of non-overlapping - norm and - norm criteria. By using a Weiszfald algorithm - norm criterion is converted to - norm criterion. So, the weighted - norm criterion is obtained for the identification. The main contributions of the paper are: (i) Presentation of the Huber`s criterion as a sum of - norm and - norm criteria; (ii) Using the Weiszfald algorithm – norm criterion is converted to a weighted - norm criterion; (iii) Weighted extended least squares in which robustness is included through weighting coefficients are derived for NARMAX (nonlinear autoregressive moving average with exogenous variable) . The illustration of the behaviour of the proposed algorithm is presented through simulations.

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A recursive hierarchical parametric estimation algorithm for nonlinear systems described by Wiener‐Hammerstein models

Cited by 5 publications

References 22 publications

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

A study on the state and parameter cooperative estimation of nonlinear mechanical systems

Robust Recursive Identification of Hammerstein Models Based on Weiszfald Algorithm

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