Nonlinear State Space Modelling of Multivariable Systems

Paduart, Johan; Schoukens, Johan; Pintelon, Rik; Coen, T.

doi:10.3182/20060329-3-au-2901.00086

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…At present, the most popular methodology to study aero elastic stability problems is to establish aero elastic system state equations [16]. How to transform the aerodynamic expression of Volterra series to state-space equations are the key techniques.…”

Section: A New Technique To Deal With Transonic Time-delayed Aerodynamic Forcesmentioning

confidence: 99%

A New Technique for establishment of Aero-Elastic Model

Wang¹,

Bing²,

Zhang³

et al. 2019

Int J Aeronaut Aerosp Eng

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The subject interest of nonlinear unsteady aerodynamics is one of great interest in the aerospace community. The interest is due to the fact that nonlinear unsteady aerodynamic behavior can have a significant effect on the performance and stability of a flight vehicle. To deal with such the aero elastic problem one generally models it by establishing state space equations. In this study, a new technique for determination of transonic nonlinear lift was developed based on discrete reduced-order Volterra series instead of relying on classical approach "eigen system realization algorithm (ERA)". In this model, one can find out that several time-delayed terms appear. Here we employ Taylor formula to expand these time-delayed terms at the current instant, and then they are vanished. In this study, we also explain how to identify discrete reducedorder kernels. As an example, the two sets of curves between the convolution kernel function method and CFD results match fine.

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Section: A New Technique To Deal With Transonic Time-delayed Aerodynamic Forcesmentioning

confidence: 99%

A New Technique for establishment of Aero-Elastic Model

Wang¹,

Bing²,

Zhang³

et al. 2019

Int J Aeronaut Aerosp Eng

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“…Let us start by motivating the use of discrete-time polynomial nonlinear state-space models. Discrete-time polynomial nonlinear state-space models are introduced in [16,27,28]. The estimation of these discrete-time models is computationally less involved than that of their continuous-time counterparts, and for control applications, discrete-time models are more suitable [28].…”

Section: Polynomial Nonlinear State-space (Pnlss) Modelsmentioning

confidence: 99%

“…The polynomial nonlinear state-space identification algorithm is introduced in [16] and further developed by [29,30,27,17]. The algorithm is explained in details in [28,16].…”

Section: Pnlss Identification Algorithmmentioning

confidence: 99%

Parameter reduction in nonlinear state-space identification of hysteresis

Esfahani,

Dreesen,

Tiels

et al. 2017

Preprint

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Hysteresis is a highly nonlinear phenomenon, showing up in a wide variety of science and engineering problems. The identification of hysteretic systems from input-output data is a challenging task. Recent work on black-box polynomial nonlinear state-space modeling for hysteresis identification has provided promising results, but struggles with a large number of parameters due to the use of multivariate polynomials. This drawback is tackled in the current paper by applying a decoupling approach that results in a more parsimonious representation involving univariate polynomials. This work is carried out numerically on input-output data generated by a Bouc-Wen hysteretic model and follows up on earlier work of the authors. The current article discusses the polynomial decoupling approach and explores the selection of the number of univariate polynomials with the polynomial degree, as well as the connections with neural network modeling. We have found that the presented decoupling approach is able to reduce the number of parameters of the full nonlinear model up to about 50%, while maintaining a comparable output error level.

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“…This is because most real-life systems can be modeled quite well with a linear model, but even better results can be obtained with a nonlinear model. The model used here [13] is developed for the identification of multivariable systems, and makes use of state space representation.…”

Section: A Model Descriptionmentioning

confidence: 99%

Nonlinear system identification on a combine harvester

Coen

Paduart

Anthonis

et al. 2006

2006 American Control Conference

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The traction system of a combine harvester contains considerable nonlinearities. The objective of this paper is to derive a model of the propulsion which can then be used for regulator design. First the nonlinearities are quantified by analyzing the output of the system excited by a multisine. Standard linear system identification techniques (such as ARX and ARMAX) are then compared to a more recent nonlinear state-space technique. Finally the derived models are validated on several alternative input signals.

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Nonlinear State Space Modelling of Multivariable Systems

Cited by 8 publications

References 9 publications

A New Technique for establishment of Aero-Elastic Model

A New Technique for establishment of Aero-Elastic Model

Parameter reduction in nonlinear state-space identification of hysteresis

Nonlinear system identification on a combine harvester

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