Identification of Hammerstein-Wiener Systems With Backlash Input Nonlinearity Bordered by Straight Lines

Brouri, Adil; Giri, F.; Ikhouane, Fayçal; Chaoui, F.Z.; Amdouri, O.

doi:10.3182/20140824-6-za-1003.00678

Cited by 26 publications

(27 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In later works, the obtained results will focus on parameters estimation of backlash and its compensation [13–17]. Moreover, extra effort will be made to extend the results obtained to sampled‐data control systems [18–20].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Observer‐based compensation control of servo systems with backlash

Sun

Zhao

Chen

2019

Asian Journal of Control

View full text Add to dashboard Cite

For compensating backlash phenomenon in servo systems, the authors propose an observer method in this paper to estimate both system states and vibration torque before controller design. First, a systematic scheme is given to obtain plant parameters, which is very important in observing system states. This is a parameter estimation principle that gives a crude estimation and computes the differences between the crude and true values. As a result, the precise value of the parameters is obtained by adding together the crude value and the difference. Then, based on the precise estimated parameters, an extended state observer (ESO) is designed to obtain feedback and feedforward signals. Consequently, robust compensation control is achieved by designing an output feedback controller, consisting of a feedback term and a feedforward term. Finally, in order to validate the proposed approach, extensive experiments are performed on a practical servo system with backlash nonlinearity.

show abstract

Section: Discussionmentioning

confidence: 99%

“…As detailed in [16], a nonlinear function fal (·) is defined as

f a l false(α, δ, υ false) = ({, \begin{matrix} left left left \\ array & array | υ {false|}^{α} s i g n (υ), & array υ > δ \\ array & array \frac{υ}{δ^{1 - α}}, & array υ \leq δ \end{matrix})

where α and δ are positive design parameters.…”

Section: Observer Designmentioning

confidence: 99%

Observer‐based compensation control of servo systems with backlash

Sun

Zhao

Chen

2019

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…Amongst those are: iterative approaches (Zhu, 2002;Vörös, 2004), overparametrization methods (Bai, 1998;Schoukens, 2015), frequency domain methods Brouri et al, 2014a), subspace methods , stochastic algorithms (Wang & Ding, 2008), blind approaches , continuous-time instrumental variable approaches (Ni et al, 2013), and maximum likelihood-based methods (Wills et al, 2013). The identification of Hammerstein-Wiener systems with a backlash input nonlinearity is discussed in (Brouri et al, 2014b).…”

Section: Hammerstein-wiener Structurementioning

confidence: 99%

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

2017

View full text Add to dashboard Cite

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.

show abstract

“…Furthermore, the smoothness assumption implies that the system nonlinearity can be developed within any interval by a polynomial decomposition [6]- [10]. Among the most used identification methods, one can find the frequency approaches [11]- [14]. Several technics can be used, e.g.…”

Section: Fig 1 Nonparametric Hammerstein Systemmentioning

confidence: 99%

Identification of Nonparametric Nonlinear Systems

Benyassi

Brouri

2019

ITM Web Conf.

View full text Add to dashboard Cite

Presently, a modelling and identification of nonlinear systems is proposed. This study is developed based on spectral approach. The proposed nonlinear system is nonparametric and can be described by Hammerstein models. These systems consist of nonlinear element followed by a linear block. This latter (the linear subsystem) is not necessarily parametric and the nonlinear function can be nonparametric smooth nonlinearity. This identification problem of Hammerstein models is studied in the presence of possibly infinite-order linear dynamics. The determination of linear and nonlinear block can be done using a unique stage.

show abstract

Identification of Hammerstein-Wiener Systems With Backlash Input Nonlinearity Bordered by Straight Lines

Cited by 26 publications

References 8 publications

Observer‐based compensation control of servo systems with backlash

Observer‐based compensation control of servo systems with backlash

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

Identification of Nonparametric Nonlinear Systems

Contact Info

Product

Resources

About