Modeling and identification of a quadrotor using LPV techniques

The aim of this project is to modelize a LPV model and to compare different identification methods. In order to accomplish that, first of all a white box model of the quadrotor has been provided. This model has been used to find a LPV description that can be used in this case. With this data various identification procedures has been tested. This project is focused in the use of particle filters to identify the parameters of the system

On the non-local geometry of turbulence

A multi-scale methodology for the study of the non-local geometry of eddy structures in turbulence is developed. Starting from a given three-dimensional field, this consists of three main steps: extraction, characterization and classification of structures. The extraction step is done in two stages. First, a multi-scale decomposition based on the curvelet transform is applied to the full three-dimensional field, resulting in a finite set of component three-dimensional fields, one per scale. Second, by iso-contouring each component field at one or more iso-contour levels, a set of closed iso-surfaces is obtained that represents the structures at that scale. The characterization stage is based on the joint probability density function (p.d.f.), in terms of area coverage on each individual iso-surface, of two differential-geometry properties, the shape index and curvedness, plus the stretching parameter, a dimensionless global invariant of the surface. Taken together, this defines the geometrical signature of the iso-surface. The classification step is based on the construction of a finite set of parameters, obtained from algebraic functions of moments of the joint p.d.f. of each structure, that specify its location as a point in a multi-dimensional ‘feature space’. At each scale the set of points in feature space represents all structures at that scale, for the specified iso-contour value. This then allows the application, to the set, of clustering techniques that search for groups of structures with a common geometry. Results are presented of a first application of this technique to a passive scalar field obtained from 5123 direct numerical simulation of scalar mixing by forced, isotropic turbulence (Reλ = 265). These show transition, with decreasing scale, from blob-like structures in the larger scales to blob- and tube-like structures with small or moderate stretching in the inertial range of scales, and then toward tube and, predominantly, sheet-like structures with high level of stretching in the dissipation range of scales. Implications of these results for the dynamical behaviour of passive scalar stirring and mixing by turbulence are discussed

Variance-constrained H∞ filtering for a class of nonlinear time-varying systems with multiple missing measurements: The finite-horizon case

Copyright [2010] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the robust H ∞ finite-horizon filtering problem for a class of uncertain nonlinear discrete time-varying stochastic systems with multiple missing measurements and error variance constraints. All the system parameters are time-varying and the uncertainty enters into the state matrix. The measurement missing phenomenon occurs in a random way, and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution in the interval . The stochastic nonlinearities under consideration here are described by statistical means which can cover several classes of well-studied nonlinearities. Sufficient conditions are derived for a finite-horizon filter to satisfy both the estimation error variance constraints and the prescribed H ∞ performance requirement. These conditions are expressed in terms of the feasibility of a series of recursive linear matrix inequalities (RLMIs). Simulation results demonstrate the effectiveness of the developed filter design scheme.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. by Grant GR/S27658/01, the Royal Society of the U.K., National Natural Science Foundation of China by Grants 60825303 and 60834003, National 973 Project of China by Grant 2009CB320600, Fok Ying Tung Education Foundation by Grant 111064, the Youth Science Fund of Heilongjiang Province of China by Grant QC2009C63, and by the Alexander von Humboldt Foundation of Germany

Damage detection in flexible plates through reduced-order modeling and hybrid particle-Kalman filtering

Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated

Optimal path shape for range-only underwater target localization using a Wave Glider

Underwater localization using acoustic signals is one of the main components in a navigation system for an autonomous underwater vehicle (AUV) as a more accurate alternative to dead-reckoning techniques. Although different methods based on the idea of multiple beacons have been studied, other approaches use only one beacon, which reduces the system’s costs and deployment complexity. The inverse approach for single-beacon navigation is to use this method for target localization by an underwater or surface vehicle. In this paper, a method of range-only target localization using a Wave Glider is presented, for which simulations and sea tests have been conducted to determine optimal parameters to minimize acoustic energy use and search time, and to maximize location accuracy and precision. Finally, a field mission is presented, where a Benthic Rover (an autonomous seafloor vehicle) is localized and tracked using minimal human intervention. This mission shows, as an example, the power of using autonomous vehicles in collaboration for oceanographic research.Peer ReviewedPostprint (author's final draft

Bayesian peak-bagging of solar-like oscillators using MCMC: A comprehensive guide

Context: Asteroseismology has entered a new era with the advent of the NASA Kepler mission. Long and continuous photometric observations of unprecedented quality are now available which have stimulated the development of a number of suites of innovative analysis tools. Aims: The power spectra of solar-like oscillations are an inexhaustible source of information on stellar structure and evolution. Robust methods are hence needed in order to infer both individual oscillation mode parameters and parameters describing non-resonant features, thus making a seismic interpretation possible. Methods: We present a comprehensive guide to the implementation of a Bayesian peak-bagging tool that employs a Markov chain Monte Carlo (MCMC). Besides making it possible to incorporate relevant prior information through Bayes' theorem, this tool also allows one to obtain the marginal probability density function for each of the fitted parameters. We apply this tool to a couple of recent asteroseismic data sets, namely, to CoRoT observations of HD 49933 and to ground-based observations made during a campaign devoted to Procyon. Results: The developed method performs remarkably well at constraining not only in the traditional case of extracting oscillation frequencies, but also when pushing the limit where traditional methods have difficulties. Moreover it provides an rigorous way of comparing competing models, such as the ridge identifications, against the asteroseismic data.Comment: Accepted for publication in A&