Rigid multibody systems have been studied extensivley due to its direct application in design and analysis of various mechanical systems such as robots and spacecraft structures. The dynamics of multibody system is governed by its equations of motion and various terms associated with it, such as the mass matrix, the generalized force vector, are well known..Forward dynamics algorithms play an important role in the simulation of multibody systems and the recursive forward dynamics algorithm for branched multibody systems is very popular. The recursive forward dynamic algorithm is highly efficient algorithm with O(n) computational complexity and scores over other algorithms when number of rigid bodies n in the system is very large. The algorithm involves finding an important mass matrix, which has been popularly termed as articulated body inertia (AB inertia). To find ijth term of any general mass matrix, we separately give virtual change to ith and jth generalized coordinates. At each point of the multibody system, the dot product of the resulting virtual displacements are taken with each other and eventually integrated over the entire multibody system, weighted by the mass. This quantity divided by the virtual changes in ith and jth coordinates gives the ijth element of the mass matrix. This is one of the fundamental ways of looking at the mass matrix. However, in literature, the AB inertia is obtained as a result of mathematical manipulation and its physical or geometrical significance from the above view point is not clear. In this thesis we present a more geometric and physical explanation for the AB inertia. The main step is to obtain a new set of generalized coordinates which relate directly to the AB inertia. We have also shown the equivalence of our method with existing methods. A comprehensive treatement on change of generalized coordinates and its effect on equations of motion has also been presented as preliminaries. The second part of the thesis deals with closed loop multibody systems.A few years ago an iterative algorithm called the sequential regularization method (SRM) was proposed for simulation of closed loop multibody systems with attractive claims on its efficiency. In literature we find that this algorithm has been implemented and studied only for planar multibody systems. As a part of the thesis work, we have developed a C-programming language code which can simulate 3-dimensional spatial multibody systems using the SRM algorithm. The programme can also perform simulation using a relatively efficient Conventional algorithm having O(n+m3) complexity, where m denotes number of closed loop constraints. Simulation studies have been carried out on a few multibody systems using the two algorithms. Some of the results have been also been validated using the commercial simulation package -ADAMS. As a result of our simulation studies, we have detected certain points, after which the solution from SRM loses it convergence. More study is required to understand this lack of convergence.

Les technologies microélectroniques ainsi que les outils de CAO actuels permettent la conception de plus en plus rapide de circuits et systèmes intégrés très complexes. L'un des plus importants problèmes rencontrés est de gérer la complexité en terme de nombre de transistors présents dans le système à manipuler ainsi qu'en terme de diversité des composants, dans la mesure où les systèmes actuels intègrent, sur un même support de type SiP ou bien SoC, de plus en plus de blocs fonctionnels hétérogènes. Le but de cette thèse est la recherche de nouvelles techniques de test qui mettent à contribution les ressources embarquées pour le test et le contrôle des modules AMS et RF. L'idée principale est de mettre en oeuvre pour ces composantes des méthodes de test et de contrôle suffisamment simples pour que les ressources numériques embarquées puissent permettre leur implémentation à faible coût. Les techniques proposées utilisent des modèles de représentation auto-régressifs qui prennent en comptes les non linéarités spécifiques à ce type de modules. Les paramètres du modèle comportemental du système sont utilisés pour la prédiction des performances du système qui sont nécessaire pour l'élaboration de la signature de test et le contrôle de la consommation du circuit. Deux démonstrateurs ont été mis en place pour valider la technique proposée : une chaine RF conçue au sein du groupe RMS et un accéléromètre de type MMA7361L. / Analogue-mixed-signal (AMS) and Radio frequency (RF) devices are required in many applications such as communications, multimedia, and signal processing. These applications are often subject to severe area constraints. The complexity of AMS and RF cores, together with shrinking device dimensions limit accessibility to the internal nodes of the circuit. This makes the test and the control of this circuit very difficult. Ensuring high test/control quality at low cost for these AMS and RF designs has become an important challenge for test engineers. RF and AMS cores are generally incorporated in a chip including large digital components as microprocessors and memories. The main idea of this work is to develop for these components simple test and control methods which can be implemented in the embedded resources of the system at low cost. The proposed techniques use autoregressive models for the devices under test/control. These models take into account the specific nonlinearities to such devices. Only the behavioural model parameters of the system are used to predict the system performances which are necessary to develop the test signature and/or control the consumption of the circuit. This method is implemented on a dsPiC30f for testing and controlling two demonstrators: a front-end RF card, designed in RMS group of TIMA laboratory, and a MMA7361L 3 axis accelerometer.

Cette thèse porte sur l'étude d'algorithmes stochastiques en grande dimension ainsi qu'à leur application en statistique robuste. Dans la suite, l'expression grande dimension pourra aussi bien signifier que la taille des échantillons étudiés est grande ou encore que les variables considérées sont à valeurs dans des espaces de grande dimension (pas nécessairement finie). Afin d'analyser ce type de données, il peut être avantageux de considérer des algorithmes qui soient rapides, qui ne nécessitent pas de stocker toutes les données, et qui permettent de mettre à jour facilement les estimations. Dans de grandes masses de données en grande dimension, la détection automatique de points atypiques est souvent délicate. Cependant, ces points, même s'ils sont peu nombreux, peuvent fortement perturber des indicateurs simples tels que la moyenne ou la covariance. On va se concentrer sur des estimateurs robustes, qui ne sont pas trop sensibles aux données atypiques. Dans une première partie, on s'intéresse à l'estimation récursive de la médiane géométrique, un indicateur de position robuste, et qui peut donc être préférée à la moyenne lorsqu'une partie des données étudiées est contaminée. Pour cela, on introduit un algorithme de Robbins-Monro ainsi que sa version moyennée, avant de construire des boules de confiance non asymptotiques et d'exhiber leurs vitesses de convergence $L^{p}$ et presque sûre.La deuxième partie traite de l'estimation de la "Median Covariation Matrix" (MCM), qui est un indicateur de dispersion robuste lié à la médiane, et qui, si la variable étudiée suit une loi symétrique, a les mêmes sous-espaces propres que la matrice de variance-covariance. Ces dernières propriétés rendent l'étude de la MCM particulièrement intéressante pour l'Analyse en Composantes Principales Robuste. On va donc introduire un algorithme itératif qui permet d'estimer simultanément la médiane géométrique et la MCM ainsi que les $q$ principaux vecteurs propres de cette dernière. On donne, dans un premier temps, la forte consistance des estimateurs de la MCM avant d'exhiber les vitesses de convergence en moyenne quadratique.Dans une troisième partie, en s'inspirant du travail effectué sur les estimateurs de la médiane et de la "Median Covariation Matrix", on exhibe les vitesses de convergence presque sûre et $L^{p}$ des algorithmes de gradient stochastiques et de leur version moyennée dans des espaces de Hilbert, avec des hypothèses moins restrictives que celles présentes dans la littérature. On présente alors deux applications en statistique robuste: estimation de quantiles géométriques et régression logistique robuste.Dans la dernière partie, on cherche à ajuster une sphère sur un nuage de points répartis autour d'une sphère complète où tronquée. Plus précisément, on considère une variable aléatoire ayant une distribution sphérique tronquée, et on cherche à estimer son centre ainsi que son rayon. Pour ce faire, on introduit un algorithme de gradient stochastique projeté et son moyenné. Sous des hypothèses raisonnables, on établit leurs vitesses de convergence en moyenne quadratique ainsi que la normalité asymptotique de l'algorithme moyenné. / This thesis focus on stochastic algorithms in high dimension as well as their application in robust statistics. In what follows, the expression high dimension may be used when the the size of the studied sample is large or when the variables we consider take values in high dimensional spaces (not necessarily finite). In order to analyze these kind of data, it can be interesting to consider algorithms which are fast, which do not need to store all the data, and which allow to update easily the estimates. In large sample of high dimensional data, outliers detection is often complicated. Nevertheless, these outliers, even if they are not many, can strongly disturb simple indicators like the mean and the covariance. We will focus on robust estimates, which are not too much sensitive to outliers.In a first part, we are interested in the recursive estimation of the geometric median, which is a robust indicator of location which can so be preferred to the mean when a part of the studied data is contaminated. For this purpose, we introduce a Robbins-Monro algorithm as well as its averaged version, before building non asymptotic confidence balls for these estimates, and exhibiting their $L^{p}$ and almost sure rates of convergence.In a second part, we focus on the estimation of the Median Covariation Matrix (MCM), which is a robust dispersion indicator linked to the geometric median. Furthermore, if the studied variable has a symmetric law, this indicator has the same eigenvectors as the covariance matrix. This last property represent a real interest to study the MCM, especially for Robust Principal Component Analysis. We so introduce a recursive algorithm which enables us to estimate simultaneously the geometric median, the MCM, and its $q$ main eigenvectors. We give, in a first time, the strong consistency of the estimators of the MCM, before exhibiting their rates of convergence in quadratic mean.In a third part, in the light of the work on the estimates of the median and of the Median Covariation Matrix, we exhibit the almost sure and $L^{p}$ rates of convergence of averaged stochastic gradient algorithms in Hilbert spaces, with less restrictive assumptions than in the literature. Then, two applications in robust statistics are given: estimation of the geometric quantiles and application in robust logistic regression.In the last part, we aim to fit a sphere on a noisy points cloud spread around a complete or truncated sphere. More precisely, we consider a random variable with a truncated spherical distribution, and we want to estimate its center as well as its radius. In this aim, we introduce a projected stochastic gradient algorithm and its averaged version. We establish the strong consistency of these estimators as well as their rates of convergence in quadratic mean. Finally, the asymptotic normality of the averaged algorithm is given.