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Rik Pintelon - One of the best experts on this subject based on the ideXlab platform.

  • Parametric Identification of parallel wiener hammerstein systems
    Automatica, 2015
    Co-Authors: Maarten Schoukens, Rik Pintelon, Gerd Vandersteen, Anna Marconato, Yves Rolain
    Abstract:

    Block-oriented nonlinear models are popular in nonlinear modeling because of their advantages to be quite simple to understand and easy to use. To increase the flexibility of single branch block-oriented models, such as Hammerstein, Wiener, and Wiener-Hammerstein models, parallel block-oriented models can be considered. This paper presents a method to identify parallel Wiener-Hammerstein systems starting from input-output data only. In the first step, the best linear approximation is estimated for different input excitation levels. In the second step, the dynamics are decomposed over a number of parallel orthogonal branches. Next, the dynamics of each branch are partitioned into a linear time invariant subsystem at the input and a linear time invariant subsystem at the output. This is repeated for each branch of the model. The static nonlinear part of the model is also estimated during this step. The consistency of the proposed initialization procedure is proven. The method is validated on real-world measurements using a custom built parallel Wiener-Hammerstein test system.

  • improved non Parametric Identification of dynamic systems excited by periodic signals the multivariate case
    Mechanical Systems and Signal Processing, 2011
    Co-Authors: Rik Pintelon, Gerd Vandersteen, Joannes Schoukens, Yves Rolain
    Abstract:

    Abstract Recently [1] a method has been developed to suppress nonParametrically the noise (and system) transients (leakage errors) in frequency response function and noise (co-)variance estimates of single-input, single-output systems excited by periodic signals. This paper extends the results of [1] to multiple-input, multiple-output systems where all inputs and outputs are disturbed by noise (i.e. an errors-in-variables framework). Two methods are presented: the first starts from multiple experiments with uncorrelated sets of inputs, and makes no assumption about the frequency response matrix (FRM); while the second only requires one single experiment, but assumes that the FRM can locally be approximated by a polynomial. Both methods estimate simultaneously the FRM, the noise level, and the level of the nonlinear distortions. For lightly damped systems, the proposed methods either significantly reduce the experiment duration or, for a given measurement time, significantly increase the frequency resolution of the FRM estimate. If the noise (and/or system) transients are the dominant error sources, then the proposed methods also significantly reduce the covariance matrix of the FRM estimates. The use of the nonParametric noise covariance estimates for Parametric transfer function modelling is also discussed in detail.

  • improved non Parametric Identification of dynamic systems excited by periodic signals
    Mechanical Systems and Signal Processing, 2011
    Co-Authors: Rik Pintelon, Gerd Vandersteen, Kurt Barbe, Joannes Schoukens
    Abstract:

    The steady state response of a system to a periodic input is still subject to noise transients. For lightly damped systems these noise transients can significantly increase the variance of the estimated frequency response function (FRF). This paper presents a method that suppresses the influence of the noise transients (leakage errors) in nonParametric FRF and noise (co-)variance estimates of dynamic systems excited by periodic signals. The method is based on a local polynomial approximation of the noise leakage errors on the FRF. Compared with the classical approaches, the proposed procedure is more robust and needs less measurement time (two signal periods are sufficient). The theory is supported by simulation and real measurement examples.

  • Parametric Identification of parallel hammerstein systems
    IEEE Transactions on Instrumentation and Measurement, 2011
    Co-Authors: Maarten Schoukens, Rik Pintelon, Yves Rolain
    Abstract:

    This paper proposes a Parametric Identification method for parallel Hammerstein systems. The linear dynamic parts of the system are modeled by a Parametric rational function in the z - or s-domain, while the static nonlinearities are represented by a linear combination of nonlinear basis functions. The Identification method uses a three-step procedure to obtain initial estimates. In the first step, the frequency response function of the best linear approximation is estimated for different input excitation levels. In the second step, the power-dependent dynamics are decomposed over a number of parallel orthogonal branches. In the last step, the static nonlinearities are estimated using a linear least squares estimation. Furthermore, an iterative Identification scheme is introduced to refine the estimates. This iterative scheme alternately estimates updated parameters for the linear dynamic systems and for the static nonlinearities. The method is illustrated on a simulation and a validation measurement example.

  • frequency domain errors in variables Identification of a time varying discrete time system
    IFAC Proceedings Volumes, 2011
    Co-Authors: John Lataire, Rik Pintelon
    Abstract:

    Abstract This paper considers the Parametric Identification of single-input single-output, linear, discrete-time, time-varying systems. The model equation is a linear ordinary difference equation with coefficients varying as polynomials in time. The model equation is formulated exactly in the frequency domain. Based on this equation a consistent estimator is constructed within an errors-in-variables framework. The estimator is illustrated on a simulation example.

Yves Rolain - One of the best experts on this subject based on the ideXlab platform.

  • Parametric Identification of parallel wiener hammerstein systems
    Automatica, 2015
    Co-Authors: Maarten Schoukens, Rik Pintelon, Gerd Vandersteen, Anna Marconato, Yves Rolain
    Abstract:

    Block-oriented nonlinear models are popular in nonlinear modeling because of their advantages to be quite simple to understand and easy to use. To increase the flexibility of single branch block-oriented models, such as Hammerstein, Wiener, and Wiener-Hammerstein models, parallel block-oriented models can be considered. This paper presents a method to identify parallel Wiener-Hammerstein systems starting from input-output data only. In the first step, the best linear approximation is estimated for different input excitation levels. In the second step, the dynamics are decomposed over a number of parallel orthogonal branches. Next, the dynamics of each branch are partitioned into a linear time invariant subsystem at the input and a linear time invariant subsystem at the output. This is repeated for each branch of the model. The static nonlinear part of the model is also estimated during this step. The consistency of the proposed initialization procedure is proven. The method is validated on real-world measurements using a custom built parallel Wiener-Hammerstein test system.

  • improved non Parametric Identification of dynamic systems excited by periodic signals the multivariate case
    Mechanical Systems and Signal Processing, 2011
    Co-Authors: Rik Pintelon, Gerd Vandersteen, Joannes Schoukens, Yves Rolain
    Abstract:

    Abstract Recently [1] a method has been developed to suppress nonParametrically the noise (and system) transients (leakage errors) in frequency response function and noise (co-)variance estimates of single-input, single-output systems excited by periodic signals. This paper extends the results of [1] to multiple-input, multiple-output systems where all inputs and outputs are disturbed by noise (i.e. an errors-in-variables framework). Two methods are presented: the first starts from multiple experiments with uncorrelated sets of inputs, and makes no assumption about the frequency response matrix (FRM); while the second only requires one single experiment, but assumes that the FRM can locally be approximated by a polynomial. Both methods estimate simultaneously the FRM, the noise level, and the level of the nonlinear distortions. For lightly damped systems, the proposed methods either significantly reduce the experiment duration or, for a given measurement time, significantly increase the frequency resolution of the FRM estimate. If the noise (and/or system) transients are the dominant error sources, then the proposed methods also significantly reduce the covariance matrix of the FRM estimates. The use of the nonParametric noise covariance estimates for Parametric transfer function modelling is also discussed in detail.

  • Parametric Identification of parallel hammerstein systems
    IEEE Transactions on Instrumentation and Measurement, 2011
    Co-Authors: Maarten Schoukens, Rik Pintelon, Yves Rolain
    Abstract:

    This paper proposes a Parametric Identification method for parallel Hammerstein systems. The linear dynamic parts of the system are modeled by a Parametric rational function in the z - or s-domain, while the static nonlinearities are represented by a linear combination of nonlinear basis functions. The Identification method uses a three-step procedure to obtain initial estimates. In the first step, the frequency response function of the best linear approximation is estimated for different input excitation levels. In the second step, the power-dependent dynamics are decomposed over a number of parallel orthogonal branches. In the last step, the static nonlinearities are estimated using a linear least squares estimation. Furthermore, an iterative Identification scheme is introduced to refine the estimates. This iterative scheme alternately estimates updated parameters for the linear dynamic systems and for the static nonlinearities. The method is illustrated on a simulation and a validation measurement example.

  • Parametric Identification of transfer functions in the frequency domain a survey
    IEEE Transactions on Automatic Control, 1994
    Co-Authors: Rik Pintelon, Joannes Schoukens, Yves Rolain, Patrick Guillaume, H Van Hamme
    Abstract:

    This paper gives a survey of frequency domain Identification methods for rational transfer functions in the Laplace (s) or z-domain. The interrelations between the different approaches are highlighted through a study of the (equivalent) cost functions. The properties of the various estimators are discussed and illustrated by several examples. >

  • Parametric Identification of transfer functions in the frequency domain a survey
    Conference on Decision and Control, 1993
    Co-Authors: Rik Pintelon, Joannes Schoukens, Yves Rolain, Patrick Guillaume, H Van Hamme
    Abstract:

    This paper gives a survey of frequency domain Identification methods for rational transfer functions in the Laplace (s) or z-domain. The interrelations between the different approaches are highlighted through a study of the (equivalent) cost functions. The properties of the various estimators are discussed and are illustrated by several examples. >

Joannes Schoukens - One of the best experts on this subject based on the ideXlab platform.

  • improved non Parametric Identification of dynamic systems excited by periodic signals the multivariate case
    Mechanical Systems and Signal Processing, 2011
    Co-Authors: Rik Pintelon, Gerd Vandersteen, Joannes Schoukens, Yves Rolain
    Abstract:

    Abstract Recently [1] a method has been developed to suppress nonParametrically the noise (and system) transients (leakage errors) in frequency response function and noise (co-)variance estimates of single-input, single-output systems excited by periodic signals. This paper extends the results of [1] to multiple-input, multiple-output systems where all inputs and outputs are disturbed by noise (i.e. an errors-in-variables framework). Two methods are presented: the first starts from multiple experiments with uncorrelated sets of inputs, and makes no assumption about the frequency response matrix (FRM); while the second only requires one single experiment, but assumes that the FRM can locally be approximated by a polynomial. Both methods estimate simultaneously the FRM, the noise level, and the level of the nonlinear distortions. For lightly damped systems, the proposed methods either significantly reduce the experiment duration or, for a given measurement time, significantly increase the frequency resolution of the FRM estimate. If the noise (and/or system) transients are the dominant error sources, then the proposed methods also significantly reduce the covariance matrix of the FRM estimates. The use of the nonParametric noise covariance estimates for Parametric transfer function modelling is also discussed in detail.

  • improved non Parametric Identification of dynamic systems excited by periodic signals
    Mechanical Systems and Signal Processing, 2011
    Co-Authors: Rik Pintelon, Gerd Vandersteen, Kurt Barbe, Joannes Schoukens
    Abstract:

    The steady state response of a system to a periodic input is still subject to noise transients. For lightly damped systems these noise transients can significantly increase the variance of the estimated frequency response function (FRF). This paper presents a method that suppresses the influence of the noise transients (leakage errors) in nonParametric FRF and noise (co-)variance estimates of dynamic systems excited by periodic signals. The method is based on a local polynomial approximation of the noise leakage errors on the FRF. Compared with the classical approaches, the proposed procedure is more robust and needs less measurement time (two signal periods are sufficient). The theory is supported by simulation and real measurement examples.

  • Parametric Identification of transfer functions in the frequency domain a survey
    IEEE Transactions on Automatic Control, 1994
    Co-Authors: Rik Pintelon, Joannes Schoukens, Yves Rolain, Patrick Guillaume, H Van Hamme
    Abstract:

    This paper gives a survey of frequency domain Identification methods for rational transfer functions in the Laplace (s) or z-domain. The interrelations between the different approaches are highlighted through a study of the (equivalent) cost functions. The properties of the various estimators are discussed and illustrated by several examples. >

  • Parametric Identification of transfer functions in the frequency domain a survey
    Conference on Decision and Control, 1993
    Co-Authors: Rik Pintelon, Joannes Schoukens, Yves Rolain, Patrick Guillaume, H Van Hamme
    Abstract:

    This paper gives a survey of frequency domain Identification methods for rational transfer functions in the Laplace (s) or z-domain. The interrelations between the different approaches are highlighted through a study of the (equivalent) cost functions. The properties of the various estimators are discussed and are illustrated by several examples. >

Sami F Masri - One of the best experts on this subject based on the ideXlab platform.

  • experimental application of on line Parametric Identification for nonlinear hysteretic systems with model uncertainty
    Structural Safety, 2010
    Co-Authors: Eleni Chatzi, Andrew W Smyth, Sami F Masri
    Abstract:

    Abstract This study presents a methodology for the on-line Identification of nonlinear hysteretic systems where not only the parameters of the system are unknown but also the nature of the analytical model describing the system is not clearly established. To this end a Bayesian approach using the Unscented Kalman Filter (UKF) method has been applied in order to investigate the effects of model complexity and parametrization. The latter can be especially challenging in the case of realistic applications involving limited information availability. The state space formulation incorporates a Bouc–Wen type hysteretic model properly modified with additional polynomial or exponential-type nonlinear terms that are properly weighted throughout the Identification procedure. The parameters associated with the candidate models might be subjected to constraints that can affect the stability of the estimation process when violated. An adaptive gain technique is introduced in order to tackle the problem of parameter boundaries. In addition, a twofold criterion based on the smoothness of the parameter prediction and the accuracy of the estimation is introduced in order to investigate the required model complexity as well as to potentially rule out ineffective terms during the Identification procedure (on-line). Previous work, Smyth et al. (1999) [1] , has dealt with the adaptive on-line Identification of nonlinear hysteretic systems using a least-squares based algorithm. The current work explores the case of more severe nonlinearities that call for the expansion of the hysteretic models commonly used in literature. The method is validated through the Identification of the highly nonlinear hysteretic behavior produced by the experimental setup described in Tasbihgoo et al. (2007) [2] involving displacement and strain (restoring force) sensor readings.

  • on line Parametric Identification of mdof nonlinear hysteretic systems
    Journal of Engineering Mechanics-asce, 1999
    Co-Authors: Andrew W Smyth, Sami F Masri, Anastasios Chassiakos, T K Caughey
    Abstract:

    A method based on adaptive estimation approaches is presented for the on-line Identification of hysteretic systems under arbitrary dynamic environments. The availability of such an Identification approach is crucial for the on-line control and monitoring of time-varying structural systems. Previous work by the writers is extended to handle the general case when no information is available on the system parameters, even the mass distribution. A robust, least-squares-based adaptive Identification algorithm, incorporating a Bouc-Wen hysteresis element model with additional polynomial-type nonlinear terms, is used to investigate the effects of persistence of excitation and of under- and overparameterization: challenging problems in realistic applications. In spite of the challenges encountered in the Identification of the hereditary nature of the restoring force of such nonlinear systems, it is shown through the use of simulation studies of single-degree-of-freedom and certain multi-degree-of-freedom systems ...

Nevzat H Ozguven - One of the best experts on this subject based on the ideXlab platform.

  • Parametric Identification of nonlinearity in structural systems using describing function inversion
    Mechanical Systems and Signal Processing, 2013
    Co-Authors: Murat Aykan, Nevzat H Ozguven
    Abstract:

    Abstract Most engineering structures include nonlinearity to some degree. Depending on the dynamic conditions and level of external forcing, sometimes a linear structure assumption may be justified. However, design requirements of sophisticated structures such as satellites may require nonlinear behavior to be considered for better performance. Therefore, it is very important to successfully detect, localize and Parametrically identify nonlinearity in such cases. In engineering applications, the location of nonlinearity and its type may not be always known in advance. Furthermore, in most of the applications in structural dynamics, linear FRF matrices constructed from experimental measurements will not be complete. These handicaps make most of the methods given in the literature difficult to apply to engineering structures. The aim of this study is to improve a previously developed method considering these practical limitations. The approach proposed can be used for detection, localization, characterization and Parametric Identification of nonlinear elements by using incomplete FRF data. In order to reduce the effort and avoid the limitations in using footprint graphs for Identification of nonlinearity, describing function inversion is used. Thus, it is made possible to identify the restoring force of more than one type of nonlinearity which may co-exist at the same location. The validation of the method is demonstrated with case studies based on simulated experiments, as well as real experiments with two nonlinear structures. It is concluded in this study that the approach proposed improves the previously developed method by avoiding the use of footprint graphs in nonlinear Identification and also by making it possible to identify more than one type of nonlinearity that may co-exist at the same location.

  • Parametric Identification of structural nonlinearities from measured frequency response data
    Mechanical Systems and Signal Processing, 2011
    Co-Authors: Ozge Arslan, Murat Aykan, Nevzat H Ozguven
    Abstract:

    Structural nonlinearity is a common phenomenon encountered in engineering structures under dynamic loading. In several cases, linear theory can suffice to analyze nonlinear systems to some extent. However, there are cases where nonlinear effects and therefore nonlinear analysis become unavoidable. In most of the engineering applications it is usually very difficult if not impossible to model nonlinearity theoretically, especially for nonlinear effects stemming from structural connections. Then it becomes necessary to detect, localize and Parametrically identify nonlinear elements from measured vibration data. In this study, two different methods, one being a method suggested recently by two of the authors of this paper, and the other being again a method developed in an earlier work, are implemented on a test rig containing a nonlinear element. Both methods are capable of Parametrically identifying nonlinearities from measured frequency response functions. It is aimed in this paper to asses the validity of each method by applying them to a real test structure and thus Parametrically identifying the nonlinear element in the system to obtain a mathematical model, and then employing the model in harmonic response analysis of the system in order to compare predicted responses with measured ones.