Location of leaks in pipelines using parameter identification tools

This work proposes an approach to locate leaks by identifying the parameters of finite models associated with these fault events. The identification problem is attacked by using well-known identification methods such as the Prediction Error Method and extended Kalman filters. In addition, a frequency evaluation is realized to check the conditions for implementing any method which require an excitation condition.Comment: This paper has some error

A Kalman Filter Approach for Biomolecular Systems with Noise Covariance Updating

An important part of system modeling is determining parameter values, particularly for biomolecular systems, where direct measurements of individual parameters are typically hard. While Extended Kalman Filters have been used for this purpose, the choice of the process noise covariance is generally unclear. In this chapter, we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise covariance on the states and parameters, as given in the Langevin framework. We adapt a Hybrid Extended Kalman Filtering technique by updating the process noise covariance at each time step based on estimates. We compare the performance of this framework with different fixed values of process noise covariance in biomolecular system models, including an oscillator model, as well as in experimentally measured data for a negative transcriptional feedback circuit. We find that the Extended Kalman Filter with such process noise covariance update is closer to the optimality condition in the sense that the innovation sequence becomes white and in achieving a balance between the mean square estimation error and parameter convergence time. The results of this chapter may help in the use of Extended Kalman Filters for systems where process noise covariance depends on states and/or parameters.Comment: 23 pages, 9 figure

On the stator flux linkage estimation of an PMSM with extended Kalman filters

The demand for drives with high quality torque control has grown tremendously in a wide variety of applications. Direct torque control (DTC) for permanent magnet synchronous motors can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled. As such the estimation of the stator flux linkage is essential. In this paper the performance of the Extended Kalman Filter (EKF) for stator flux linkage estimation is studied. Starting from a formulation of the EKF for isotropic motors, the influence of rotor anisotropy and saturation is evaluated. Subsequently it is expanded to highly isotropic motors as well. In both cases the possibilities to add parameter estimations are evaluated

Localization Using Extended Kalman Filters in Wireless Sensor Networks

Introduction: Localization arises repeatedly in many location-aware applications such as navigation, autonomous robotic movement, and asset tracking. Analytical localization methods include triangulation and trilateration. Triangulation uses angles, distances, and trigonometric relationships to locate an object. Trilateration, on the other hand, uses only distance measurements to identify the position of the target

Simple and extended Kalman filters : an application to term structures of commodity prices.

This article presents and compares two different Kalman filters. These methods provide a very interesting way to cope with the presence of non-observable variables, which is a frequent problem in finance. They are also very fast even in the presence of a large information volume. The first filter presented, which corresponds to the simplest version of a Kalman filter, can be used solely in the case of linear models. The second filter - the extended one - is a generalization of the first one, and it enables one to deal with non-linear models. However, it also introduces an approximation in the analysis, whose possible influence must be appreciated. The principles of the method and its advantages are first presented. It is then explained why it is interesting in the case of term structure models of commodity prices. Choosing a well-known term structure model, practical implementation problems are discussed and tested. Finally, in order to appreciate the impact of the approximation introduced for non-linear models, the two filters are compared.Term Structure; Commodity Future Prices; Kalman Filter;

On extended Kalman filters with augmented state vectors for the stator flux estimation in SPMSMs

The demand for highly dynamic electrical drives, characterized by high quality torque control, in a wide variety of applications has grown tremendously during the past decades. Direct torque control (DTC) for permanent magnet synchronous motors (PMSM) can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled, based on torque and flux errors. As such the estimation of the stator flux linkage is essential. In the literature several possible solutions for the estimation of the stator flux linkage are proposed. In order to overcome problems associated with the integration of the back-emf, the use of state observers has been advocated in the literature. Several types of state observers have been conceived and implemented for PMSMs, especially the Extended Kalman Filter (EKF) has received much attention. In most reported applications however the EKF is only used to estimate the speed and rotor position of the PMSM in order to realize field oriented current control in a rotor reference frame. Far fewer publications mention the use of an EKF to estimate the stator flux linkage vector in order to apply DTC. Still the performance of the EKF in the estimation of the stator flux linkage vector has not yet been thoroughly investigated. In this paper the performance of the EKF for stator flux linkage is studied and simulated. The possibilities to improve the estimation by augmenting the state vector and the consequences of these alterations are explored. Important practical aspects for FPGA implementation are discussed

Novel Convergence Results in Nonlinear Filtering

In this dissertation, the discrete-time extended Kalman filter is analyzed for its ability to attenuate finite-energy disturbances, known as the H-infinity property. Though the extended Kalman filter is designed to be a locally optimal minimum variance estimator, this dissertation proves that it has additional properties, such as H-infinity. This analysis is performed with the extended Kalman filter in direct form. Since this form reduces assumptions placed on the system in previous works on convergence and H-2 properties of the extended Kalman filter, the extended Kalman filter used as a nonlinear observer for noise-free models is revisited using the direct form to demonstrate these properties. Additionally, two representations for the discrete-time uncertain measurement model with finite-energy disturbances are considered: 1) each sensor in the measurement can fail independently with different failure rates and 2) all of the sensors in the measurement fail at the same time. The discrete-time extended Kalman filters designed for such models are analyzed for general convergence, the H-2 property, and the H-infinity property. As an extension of this work, the continuous-time extended Kalman filter is applied to systems with finite-energy disturbances. This continuous-time extended Kalman filter is shown to inherently have the H-infinity property. Simulation studies have been performed on all of the extended Kalman filters in this dissertation. These simulation studies demonstrate that when the extended Kalman filters converge, they will also exhibit the H-2 and H-infinity properties. The bounds developed on these properties are affected by the same constraints that affect convergence, i.e. magnitudes of the initial estimation error and the disturbance as well as the severity of the nonlinearities in the model