Modelling Errors (modelling + error)

Distribution by Scientific Domains


Selected Abstracts


Parameter estimation accuracy analysis for induction motors

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 2 2005
E. Laroche
Abstract Various analytical dynamic models of induction machines, some of which take magnetic saturation and iron loss into account, are available in the literature. When parameter estimation is required, models must not only be theoretically identifiable but allow for accurate parameter estimation as well. This paper presents a comparison of parameter estimation accuracies obtained using different models and sets of measurements in the case of steady-state sinusoidal measurements. An explicit expression of estimation error is established and evaluated with respect to several measurement and modelling errors. This study will show that certain models are better suited for identification purposes than others and that certain sensors are bound to be more accurate than others. Lastly, an optimal experimental design procedure is implemented in order to derive an improved measurement set that leads to reduced estimation errors. Copyright © 2005 John Wiley & Sons, Ltd. [source]


A pattern-based approach for multiple removal applied to a 3D Gulf of Mexico data set

GEOPHYSICAL PROSPECTING, Issue 2 2006
Antoine Guitton
ABSTRACT Surface-related multiples are attenuated for one sail line and one streamer of a 3D data set (courtesy of Compagnie Générale de Géophysique). The survey was carried out in the Gulf of Mexico in the Green Canyon area where salt intrusions close to the water-bottom are present. Because of the complexity of the subsurface, a wavefield method incorporating the full 3D volume of the data for multiple removal is necessary. This method comprises modelling of the multiples, where the data are used as a prediction operator, and a subtraction step, where the model of the multiples is adaptively removed from the data with matching filters. The accuracy of the multiple model depends on the source/receiver coverage at the surface. When this coverage is not dense enough, the multiple model contains errors that make successful subtraction more difficult. In these circumstances, one can either (1) improve the modelling step by interpolating the missing traces, (2) improve the subtraction step by designing methods that are less sensitive to modelling errors, or (3) both. For this data set, the second option is investigated by predicting the multiples in a 2D sense (as opposed to 3D) and performing the subtraction with a pattern-based approach. Because some traces and shots are missing for the 2D prediction, the data are interpolated in the in-line direction using a hyperbolic Radon transform with and without sparseness constraints. The interpolation with a sparseness constraint yields the best multiple model. For the subtraction, the pattern-based technique is compared with a more standard, adaptive-subtraction scheme. The pattern-based approach is based on the estimation of 3D prediction-error filters for the primaries and the multiples, followed by a least-squares estimation of the primaries. Both methods are compared before and after prestack depth migration. These results suggest that, when the multiple model is not accurate, the pattern-based method is more effective than adaptive subtraction at removing surface-related multiples while preserving the primaries. [source]


Filter-based fault detection and diagnosis using output PDFs for stochastic systems with time delays

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2006
Y. M. Zhang
Abstract In this paper, a fault detection and diagnosis (FDD) scheme is studied for general stochastic dynamic systems subjected to state time delays. Different from the formulation of classical FDD problems, it is supposed that the measured information for the FDD is the probability density function (PDF) of the system output rather than its actual value. A B-spline expansion technique is applied so that the output PDF can be formulated in terms of the dynamic weights of the B-spline expansion, by which a time delay model can be established between the input and the weights with non-linearities and modelling errors. As a result, the concerned FDD problem can be transformed into a classic FDD problem subject to an uncertain non-linear system with time delays. Feasible criteria to detect the system fault are obtained and a fault diagnosis method is further presented to estimate the fault. Simple simulations are given to demonstrate the efficiency of the proposed approach. Copyright © 2006 John Wiley & Sons, Ltd. [source]


On robust stability of uncertain systems with multiple time-delays

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 15 2010
Tong ZhouArticle first published online: 27 NOV 200
Abstract On the basis of an infinite to one mapping and the structure of the null space of a multivariate matrix polynomial (MMP), a novel sufficient condition is derived in this paper for the robust stability of a linear time-invariant system with multiple uncertain time-delays, parametric modelling errors and unmodelled dynamics. This condition depends on time-delay bounds and is less conservative than the existing ones. An attractive property is that this condition becomes also necessary in some physically meaningful situations, such as the case that there is only one uncertain time-delay and neither parametric perturbations nor unmodelling errors exist. Moreover, using ideas of representing a positive-definite MMP through matrix sum of squares, an asymptotic necessary and sufficient condition is derived for the robust stability of this system. All the conditions can be converted to linear matrix inequalities. Copyright © 2009 John Wiley & Sons, Ltd. [source]


Robust monotone gradient-based discrete-time iterative learning control

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 6 2009
D. H. Owens
Abstract This paper considers the use of matrix models and the robustness of a gradient-based iterative learning control (ILC) algorithm using both fixed learning gains and nonlinear data-dependent gains derived from parameter optimization. The philosophy of the paper is to ensure monotonic convergence with respect to the mean-square value of the error time series. The paper provides a complete and rigorous analysis for the systematic use of the well-known matrix models in ILC. Matrix models provide necessary and sufficient conditions for robust monotonic convergence. They also permit the construction of accurate sufficient frequency domain conditions for robust monotonic convergence on finite time intervals for both causal and non-causal controller dynamics. The results are compared with recently published results for robust inverse-model-based ILC algorithms and it is seen that the algorithm has the potential to improve the robustness to high-frequency modelling errors, provided that resonances within the plant bandwidth have been suppressed by feedback or series compensation. Copyright © 2008 John Wiley & Sons, Ltd. [source]


Pressure and temperature-based adaptive observer of air charge for turbocharged diesel engines

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 6 2004
A. G. Stefanopoulou
Abstract In this paper we design an adaptive air charge estimator for turbocharged diesel engines using intake manifold pressure, temperature and engine speed measurements. This adaptive observer scheme does not depend on mass air flow sensors and can be applied to diesel engines with no exhaust gas recirculation (EGR). The performance of the adaptive scheme is shown in simulations to be comparable to conventional air charge estimation schemes if perfect temperature measurements are available. The designed scheme cannot estimate fast transients and its performance deteriorates with temperature sensor lags. Despite all these difficulties, this paper demonstrates that (i) the proposed scheme has better robustness to modelling errors because it provides a closed-loop observer design, and (ii) robust air charge estimation is achievable even without air flow sensors if good (fast) temperature sensors become available. Finally, we provide a rigorous proof and present the implementation challenges as well as the limiting factors of this adaptation scheme and point to hardware and temperature sensor requirements. Copyright © 2004 John Wiley & Sons, Ltd. [source]


AN ITERATIVE LMI APPROACH TO RFDF FOR LINEAR SYSTEM WITH TIME-VARYING DELAYS

ASIAN JOURNAL OF CONTROL, Issue 1 2006
Maiying Zhong
ABSTRACT This paper deals with robust fault detection filter (RFDF) problem for a class of linear uncertain systems with time-varying delays and model uncertainties. The RFDF design problem is formulated as an optimization problem by using L2 -induced norm to represent the robustness of residual to unknown inputs and modelling errors, and the sensitivity to faults. A sufficient condition to the solvability of formulated problem is established in terms of certain matrix inequalities, which can be solved with the aid of an iterative linear matrix inequality (ILMI) algorithm. Finally, a numerical example is given to illustrate the effectiveness of the proposed method. [source]