The Experts below are selected from a list of 174093 Experts worldwide ranked by ideXlab platform
Jonas Sjöberg - One of the best experts on this subject based on the ideXlab platform.
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Maximum Likelihood identification of Wiener-Hammerstein system with process noise
IFAC-PapersOnLine, 2018Co-Authors: Giuseppe Giordano, Jonas SjöbergAbstract:The Wiener-Hammerstein model is a block-oriented model consisting of two linear blocks and a static nonlinearity in the middle. We address the identification problem of this model, when a disturbance affects the input of the non-linearity, i.e. process noise. For this case, a Maximum Likelihood estimator is derived, which delivers a Consistent Estimate of the model parameters. In the presence of process noise, in fact, a standard Prediction Error Method normally leads to biased results. The Maximum Likelihood Estimate is then used together with the Best Linear Approximation of the system, in order to implement a complete identification scheme when the parametrization of the linear blocks is not known a priori. The computation of the likelihood function requires numerical integration, which is solved by Monte Carlo and Metropolis-Hastings techniques. Numerical examples show the effectiveness of the identification scheme.
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CDC - Consistency aspects of Wiener-Hammerstein model identification in presence of process noise
2016 IEEE 55th Conference on Decision and Control (CDC), 2016Co-Authors: Giuseppe Giordano, Jonas SjöbergAbstract:The Wiener-Hammerstein model is a block-oriented model consisting of two linear blocks and a static nonlinearity in the middle. Several identification approaches for this model structure rely on the fact that the best linear approximation of the system is a Consistent Estimate of the two linear parts, under the hypothesis of Gaussian excitation. But, these approaches do not consider the presence of other disturbance sources than measurement noise. In this paper we consider the presence of a disturbance entering before the nonlinearity (process noise) and we show that, also in this case, the best linear approximation is a Consistent Estimate of underlying linear dynamics. Furthermore, we analyse the impact of the process noise on the nonlinearity estimation, showing that a standard prediction error method approach can lead to biased results.
Marco Lovera - One of the best experts on this subject based on the ideXlab platform.
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Convergence analysis of instrumental variable recursive subspace identification algorithms
Automatica, 2007Co-Authors: Guillaume Mercère, Marco LoveraAbstract:The convergence properties of recently developed recursive subspace identification methods are investigated in this paper. The algorithms operate on the basis of instrumental variable (IV) versions of the propagator method for signal subspace estimation. It is proved that, under suitable conditions on the input signal and the system, the considered recursive subspace identification algorithms converge to a Consistent Estimate of the propagator and, by extension, to the state-space system matrices.
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Convergence analysis of instrumental variable recursive subspace identification algorithms
2006Co-Authors: Guillaume Mercère, Marco LoveraAbstract:The convergence properties of a recently developed recursive subspace identification algorithm of the MOESP class are investigated in this paper. The algorithm operates on the basis of an extended instrumental variable (EIV) version of the propagator method for signal subspace estimation. It is proved that, under weak conditions on the input signal and the identified system, the considered recursive subspace identification algorithm converges to a Consistent Estimate of the propagator and, by extension, of the state space system matrices
Giuseppe Giordano - One of the best experts on this subject based on the ideXlab platform.
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Maximum Likelihood identification of Wiener-Hammerstein system with process noise
IFAC-PapersOnLine, 2018Co-Authors: Giuseppe Giordano, Jonas SjöbergAbstract:The Wiener-Hammerstein model is a block-oriented model consisting of two linear blocks and a static nonlinearity in the middle. We address the identification problem of this model, when a disturbance affects the input of the non-linearity, i.e. process noise. For this case, a Maximum Likelihood estimator is derived, which delivers a Consistent Estimate of the model parameters. In the presence of process noise, in fact, a standard Prediction Error Method normally leads to biased results. The Maximum Likelihood Estimate is then used together with the Best Linear Approximation of the system, in order to implement a complete identification scheme when the parametrization of the linear blocks is not known a priori. The computation of the likelihood function requires numerical integration, which is solved by Monte Carlo and Metropolis-Hastings techniques. Numerical examples show the effectiveness of the identification scheme.
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CDC - Consistency aspects of Wiener-Hammerstein model identification in presence of process noise
2016 IEEE 55th Conference on Decision and Control (CDC), 2016Co-Authors: Giuseppe Giordano, Jonas SjöbergAbstract:The Wiener-Hammerstein model is a block-oriented model consisting of two linear blocks and a static nonlinearity in the middle. Several identification approaches for this model structure rely on the fact that the best linear approximation of the system is a Consistent Estimate of the two linear parts, under the hypothesis of Gaussian excitation. But, these approaches do not consider the presence of other disturbance sources than measurement noise. In this paper we consider the presence of a disturbance entering before the nonlinearity (process noise) and we show that, also in this case, the best linear approximation is a Consistent Estimate of underlying linear dynamics. Furthermore, we analyse the impact of the process noise on the nonlinearity estimation, showing that a standard prediction error method approach can lead to biased results.
Guillaume Mercère - One of the best experts on this subject based on the ideXlab platform.
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Convergence analysis of instrumental variable recursive subspace identification algorithms
Automatica, 2007Co-Authors: Guillaume Mercère, Marco LoveraAbstract:The convergence properties of recently developed recursive subspace identification methods are investigated in this paper. The algorithms operate on the basis of instrumental variable (IV) versions of the propagator method for signal subspace estimation. It is proved that, under suitable conditions on the input signal and the system, the considered recursive subspace identification algorithms converge to a Consistent Estimate of the propagator and, by extension, to the state-space system matrices.
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Convergence analysis of instrumental variable recursive subspace identification algorithms
2006Co-Authors: Guillaume Mercère, Marco LoveraAbstract:The convergence properties of a recently developed recursive subspace identification algorithm of the MOESP class are investigated in this paper. The algorithm operates on the basis of an extended instrumental variable (EIV) version of the propagator method for signal subspace estimation. It is proved that, under weak conditions on the input signal and the identified system, the considered recursive subspace identification algorithm converges to a Consistent Estimate of the propagator and, by extension, of the state space system matrices
Federico Morelli - One of the best experts on this subject based on the ideXlab platform.
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Closed-loop Identification of MIMO Systems in the Prediction Error Framework: Data Informativity Analysis
Automatica, 2020Co-Authors: Kévin Colin, Laurent Bako, Xavier Bombois, Federico MorelliAbstract:In the Prediction Error Identification framework, it is essential that the experiment yields informative data with respect to the chosen model structure to get a Consistent Estimate. In this work, we focus on the data informativity property for the identification of Multi-Inputs Multi-Outputs system in closed-loop and we derive conditions to verify if a given external excitation combined with the feedback introduced by the controller yields informative data with respect to the model structure. This study covers the case of the classical model structures used in prediction-error identification and the classical types of external excitation vectors, i.e., vectors whose elements are either multisine or filtered white noises.
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Data Informativity for the Open-Loop Identification of MIMO Systems in the Prediction Error Framework
Automatica, 2020Co-Authors: Kévin Colin, Laurent Bako, Xavier Bombois, Federico MorelliAbstract:In Prediction Error identification, to obtain a Consistent Estimate of the true system, it is crucial that the input excitation yields informative data with respect to the chosen model structure. We consider in this paper the data informativity property for the identification of a Multiple-Input Multiple-Output system in open loop and we derive conditions to check whether a given input vector will yield informative data with respect to the chosen model structure. We do that for the classical model structures used in prediction-error identification and for the classical types of input vectors, i.e., input vectors whose elements are either multisines or filtered white noises.
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Data Informativity for the Identication of MISO FIR Systems with Filtered White Noise Excitation
2019Co-Authors: Kévin Colin, Laurent Bako, Xavier Bombois, Federico MorelliAbstract:For Prediction Error Identication, there are two main ingredients to get a Consistent Estimate: one of them is the data informativity with respect to (w.r.t.) the considered model structure. One common criterion used for the informativity is the positive deniteness of the input density spectral power (DSP) matrix at all frequencies. This criterion is not appropriate for multisine excitation but can be used for ltered white noise excitation for many identication problems. However, this criterion is not necessary and its application for some identication problems might not be possible. In this paper, we propose a necessary and sucient condition for the data informativity in the case of multiple-inputs single-output (MISO) nite impulse response (FIR) model structure in open-loop.
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Data Informativity for the Identication of particular Parallel Hammerstein Systems
2019Co-Authors: Kévin Colin, Laurent Bako, Xavier Bombois, Federico MorelliAbstract:To obtain a Consistent Estimate when performing an identication with Prediction Error, it is important that the excitation yields informative data with respect to the chosen model structure. While the characterization of this property seems to be a mature research area in the linear case, the same cannot be said for nonlinear systems. In this work, we study the data informativity for a particular type of Hammerstein systems for two commonly-used excitations: white Gaussian noise and multisine. The real life example of the MEMS gyroscope is considered.
