Search results for "EXTENDED KALMAN FILTER"
showing 10 items of 44 documents
Sensorless control of induction motors using an extended Kalman filter and linear quadratic tracking
2017
Induction motors are the most commonly used prime-movers in industrial applications. Many induction motors supplied by frequency converters are coupled with a physical angular rotor position/velocity sensor which makes the drive complex and require maintenance. This paper presents a sensorless control structure to avoid using a physical angular rotor position/velocity sensor. The proposed method estimates and control the angular rotor velocity using optimal control theory. The optimal controller used in this paper is based on linear quadratic tracking and the states of the machine are estimated using an extended Kalman filter. Both the controller and the estimator utilize the same internal …
Parameter identification and state estimation of a microalgae dynamical model in sulphur deprived conditions: Global sensitivity analysis, optimizati…
2014
International audience; In this article, a dynamic model describing the growth of the green microalgae Chlamydomonas reinhardtii , under light attenuation and sulphur‐deprived conditions leading to hydrogen production in a photobioreactor is presented. The strong interactions between biological and physical phenomena require complex mathematical expressions with an important number of parameters. This article presents a global identification procedure in three steps using data from batch experiments. First, it includes the application of a sensitivity function analysis, which allows one to determine the parameters having the greatest influence on model outputs. Secondly, the most influentia…
Adaptive-gain extended Kalman filter: Extension to the continuous-discrete case
2009
In the present article we propose a nonlinear observer that merges the behaviors 1) of an extended Kalman filter, mainly designed to smooth off noise , and 2) of high-gain observers devoted to handle large perturbations in the state estimation. We specifically aim at continuous-discrete systems. The strategy consists in letting the high-gain self adapt according to the innovation. We define innovation computed over a time window and justify its usage via an important lemma. We prove the general convergence of the resulting observer.
Adaptive Consensus-Based Distributed Kalman Filter for WSNs with Random Link Failures
2016
Wireless Sensor Networks have emerged as a very powerful tool for the monitoring and control, over large areas, of diverse phenomena. One of the most appealing properties of these networks is their potentiality to perform complex tasks in a total distributed fashion, without requiring a central entity. In this scenario, where nodes are constrained to use only local information and communicate with one-hop neighbors, iterative consensus algorithms are extensively used due to their simplicity. In this work, we propose the design of a consensus-based distributed Kalman filter for state estimation, in a sensor network whose connections are subject to random failures. As a result of this unrelia…
Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter
2019
In this paper, a solution for estimating the relative position and orientation between two ships in six degrees-of-freedom (6DOF) using sensor fusion and an extended Kalman filter (EKF) approach is presented. Two different sensor types, based on time-of-flight and inertial measurement principles, were combined to create a reliable and redundant estimate of the relative motion between the ships. An accurate and reliable relative motion estimate is expected to be a key enabler for future ship-to-ship operations, such as autonomous load transfer and handling. The proposed sensor fusion algorithm was tested with real sensors (two motion reference units (MRS) and a laser tracker) and an experime…
Automatic Take Off and Landing for UAS Flying in Turbulent Air - An EKF Based Procedure
2020
An innovative use of the Extended Kalman Filter (EKF) is proposed to perform automatic take off and landing by the rejection of disturbances due to turbulence. By using two simultaneously working Extended Kalman Filters, a procedure is implemented: the first filter, by using measurements gathered in turbulent air, estimates wind components; the second one, by using the estimated disturbances, obtains command laws that are able to reject disturbances. The fundamental innovation of such a procedure consists in the fact that the covariance matrices of process (Q) and measurement (R) noise are not treated as filter design parameters. In this way determined optimal values of the aforementioned m…
Using an Adaptive High-Gain Extended Kalman Filter With a Car Efficiency Model
2010
The authors apply the Adaptive High-Gain Extended Kalman Filter (AEKF) to the problem of estimating engine efficiency with data gathered from normal driving. The AEKF is an extension of the traditional Kalman Filter that allows the filter to be reactive to perturbations without sacrificing noise filtering. An observability normal form of the engine efficiency model is developed for the AEKF. The continuous-discrete AEKF is presented along with strategies for dealing with asynchronous data. Empiric test results are presented and contrasted with EKF-derived results.Copyright © 2010 by ASME
Wind component estimation for UAS flying in turbulent air
2019
One of the most important problem of autonomous flight for UAS is the wind identification, especially for small scale vehicles. This research focusses on an identification methodology based on the Extended Kalman Filter (EKF). In particular authors focus their attention on.the filter tuning problem. The proposed procedure requires low computational power, so it is very useful for UAS. Besides it allows a robust wind component identification even when, as it is usually, the measurement data set is affected by noticeable noises. (C) 2019 Elsevier Masson SAS. All rights reserved.
Optimal Flight Path Determination in Turbulent Air: A Modified EKF Approach
2018
By using the Extended Kalman Filter an accurate path following in turbulent air is performed. The procedure employs simultaneously two dierent EKFs: the rst one estimates disturbances, the second one aords to determine the necessary controls displacements for rejecting those ones. To tune the EKFs an optimization algorithm has been designed to automatically determine Process Noise Covariance and Measurement Noise Covariance matrices. The rst lter, by using instrumental measurements gathered in turbulent air, estimates wind components. The second one obtains command laws able to follow the desired ight path. To perform this task aerodynamic coecients have been modied. Such a procedure leads …
AN EXTENDED KALMAN FILTER BASED TECHNIQUE FOR ON-LINE IDENTIFICATION OF UAS PARAMETERS.
2015
The present article deals with the identification,at the same time, of aircraft stability and control parameters taking into account dynamic damping derivatives. Such derivatives,due to the rate of change of the angle of attack, are usually neglected. So the damping characteristics of aircraft dynamics are attributed only on pitch rate derivatives. To cope with the dynamic effects of these derivatives, authors developed devoted procedures to estimate them. In the present paper, a complete model of aerodynamic coefficients has been tuned-up to identify simultaneously the whole set of derivatives. Besides, in spite of the employed reduced order model and/or decoupled dynamics, a six degrees o…