5,619 research outputs found
Grey-box state-space identification of nonlinear mechanical vibrations
The present paper deals with the identification of nonlinear mechanical
vibrations. A grey-box, or semi-physical, nonlinear state-space representation
is introduced, expressing the nonlinear basis functions using a limited number
of measured output variables. This representation assumes that the observed
nonlinearities are localised in physical space, which is a generic case in
mechanics. A two-step identification procedure is derived for the grey-box
model parameters, integrating nonlinear subspace initialisation and weighted
least-squares optimisation. The complete procedure is applied to an electrical
circuit mimicking the behaviour of a single-input, single-output (SISO)
nonlinear mechanical system and to a single-input, multiple-output (SIMO)
geometrically nonlinear beam structure
Efficient Parameterization of Nonlinear System Models:a Comment on Noel and Schoukens
Nöel, J. P., & Schoukens, J. [2018. Grey-box state-space identification of nonlinear mechanical vibrations. International Journal of Control, 91, 1–22] discuss a methodology for the discrete-time state-space identification of nonlinear systems and apply this to experimental data from the well known Silverbox nonlinear circuit, producing a model characterised by 13 parameters. This model explains the data very well but the parameter estimates are not well defined in the optimisation results, with the very large confidence bounds suggesting that the model is over-parameterised. This comment shows that this is indeed the case and that the data can be explained equally well by an alternative continuous-time, State-Dependent Parameter (SDP) transfer function model with only 6 parameters, the estimates of which are well defined with very tight confidence bounds. The comment also raises questions about how the model form for nonlinear systems such as the Silverbox should be identified and suggests that the Data-Based Mechanistic (DBM) approach to modelling has some advantages in this regard
Parameter reduction in nonlinear state-space identification of hysteresis
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.Comment: 24 pages, 8 figure
Identification of Nonlinear Normal Modes of Engineering Structures under Broadband Forcing
The objective of the present paper is to develop a two-step methodology
integrating system identification and numerical continuation for the
experimental extraction of nonlinear normal modes (NNMs) under broadband
forcing. The first step processes acquired input and output data to derive an
experimental state-space model of the structure. The second step converts this
state-space model into a model in modal space from which NNMs are computed
using shooting and pseudo-arclength continuation. The method is demonstrated
using noisy synthetic data simulated on a cantilever beam with a
hardening-softening nonlinearity at its free end.Comment: Journal pape
Pitting damage levels estimation for planetary gear sets based on model simulation and grey relational analysis
The planetary gearbox is a critical mechanism in helicopter transmission systems. Tooth failures in planetary gear sets will cause great risk to helicopter operations. A gear pitting damage level estimation methodology has been devised in this paper by integrating a physical model for simulation signal generation, a three-step statistic algorithm for feature selection and damage level estimation for grey relational analysis. The proposed method was calibrated firstly with fault seeded test data and then validated with the data of other tests from a planetary gear set. The estimation results of test data coincide with the actual test records, showing the effectiveness and accuracy of the method in providing a novel way to model based methods and feature selection and weighting methods for more accurate health monitoring and condition prediction
Modelling and Identification for Control of Gas Bearings
Gas bearings are popular for their high speed capabilities, low friction and clean operation, but suffer from poor damping, which poses challenges for safe operation in presence of disturbances Feedback control can achieve enhanced damping but requires low complexity models of the dominant dynamics over its entire envelope of operation. Models from first principles are complex and sensitive to parameter uncertainty. This paper presents an experimental technique for ”in situ” identification of a low complexity model of a rotor–bearing–actuator system and demonstrates identification over relevant ranges of rotational speed and gas injection pressure This is obtained using Parameter-varying linear models that are found to capture the dominant dynamics. The approach is shown to be easily applied and to suit subsequent control design. Based on the identified models, decentralised proportional control is designed and shown to obtain the required damping in theory and in a laboratory test rig
Nonlinear system-identification of the filling phase of a wet-clutch system
The work presented illustrates how the choice of input perturbation signal and experimental design improves the derived model of a nonlinear system, in particular the dynamics of a wet-clutch system. The relationship between the applied input current signal and resulting output pressure in the filling phase of the clutch is established based on bandlimited periodic signals applied at different current operating points and signals approximating the desired filling current signal. A polynomial nonlinear state space model is estimated and validated over a range of measurements and yields better fits over a linear model, while the performance of either model depends on the perturbation signal used for model estimation
Modeling and experimental identification of vibrating structures: localized and distributed nonlinearities
L'abstract è presente nell'allegato / the abstract is in the attachmen
- …