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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Average consensus in matrix-weight-balanced digraphs

Allapanda, Chinnappa Yogesh B. 11 April 2019 (has links)
This thesis investigates the average consensus of multi-agent systems with linear dynamics whose interconnections are modelled by balanced digraphs with matrix- weights. The thesis first introduces the notion of balanced digraphs and mirror graphs for matrix weights. Then it proves that the matrix-weight-balanced con- sensus controller is indeed globally asymptotically stable. The Lyapunov stability analysis exploits the properties of the mirror graph of a balanced digraph. Further, the necessary and sufficient condition for the system to achieve average consensus is shown to be positive definiteness of the matrix weights of its balanced digraph. Simulations with robots in SIMULINK verify that positive definite matrix weights on balanced graphs are indeed necessary and sufficient for average consensus. Fi- nally formation control of a multi-robot system is shown to be an application of the matrix-weight-balanced consensus algorithm using real time simulation of Clearpath Ridgeback robots in Gazebo and ROS. / Graduate
2

Dynamic Neural Network-based Adaptive Inverse Optimal Control Design

Alhejji, Ayman Khalid 01 August 2014 (has links)
This dissertation introduces a Dynamical Neural Network (DNN) model based adaptive inverse optimal control design for a class of nonlinear systems. A DNN structure is developed and stabilized based on a control Lyapunov function (CLF). The CLF must satisfy the partial Hamilton Jacobi-Bellman (HJB) equation to solve the cost function in order to prove the optimality. In other words, the control design is derived from the CLF and inversely achieves optimality when the given cost function variables are determined posterior. All the stability of the closed loop system is ensured using the Lyapunov-based analysis. In addition to structure stability, uncertainty/ disturbance presents a problem to a DNN in that it could degrade the system performance. Therefore, the DNN needs a robust control against uncertainty. Sliding mode control (SMC) is added to nominal control design based CLF in order to stabilize and counteract the effects of disturbance from uncertain DNN, also to achieve global asymptotic stability. In the next section, a DNN observer is considered for estimating states of a class of controllable and observable nonlinear systems. A DNN observer-based adaptive inverse optimal control (AIOC) is needed. With weight adaptations, an adaptive technique is introduced in the observer design and its stabilizing control. The AIOC is designed to control a DNN observer and nonlinear system simultaneously while the weight parameters are updated online. This control scheme guarantees the quality of a DNN's state and minimizes the cost function. In addition, a tracking problem is investigated. An inverse optimal adaptive tracking control based on a DNN observer for unknown nonlinear systems is proposed. Within this framework, a time-varying desired trajectory is investigated, which generates a desired trajectory based on the external inputs. The tracking control design forces system states to follow the desired trajectory, while the DNN observer estimates the states and identifies unknown system dynamics. The stability method based on Lyapunov-based analysis is guaranteed a global asymptotic stability. Numerical examples and simulation studies are presented and shown for each section to validate the effectiveness of the proposed methods.
3

Inertial Gradient-Descent algorithms for convex minimization / Algorithmes de descente de gradient inertiels pour la minimisation convexe.

Apidopoulos, Vasileios 11 October 2019 (has links)
Cette thèse porte sur l’étude des méthodes inertielles pour résoudre les problèmes de minimisation convexe structurés. Depuis les premiers travaux de Polyak et Nesterov, ces méthodes sont devenues très populaires, grâce à leurs effets d’accélération. Dans ce travail, on étudie une famille d’algorithmes de gradient proximal inertiel de type Nesterov avec un choix spécifique de suites de sur-relaxation. Les différentes propriétés de convergence de cette famille d’algorithmes sont présentées d’une manière unifiée, en fonction du paramètre de sur-relaxation. En outre, on étudie ces propriétés, dans le cas des fonctions lisses vérifiant des hypothèses géométriques supplémentaires, comme la condition de croissance (ou condition de Łojasiewicz). On montre qu’en combinant cette condition de croissance avec une condition de planéité (flatness) sur la géométrie de la fonction minimisante, on obtient de nouveaux taux de convergence. La stratégie adoptée ici, utilise des analogies du continu vers le discret, en passant des systèmes dynamiques continus en temps à des schémas discrets. En particulier, la famille d’algorithmes inertiels qui nous intéresse, peut être identifiée comme un schéma aux différences finies d’une équation/inclusion différentielle. Cette approche donne les grandes lignes d’une façon de transposer les différents résultats et leurs démonstrations du continu au discret. Cela ouvre la voie à de nouveaux schémas inertiels possibles, issus du même système dynamique. / This Thesis focuses on the study of inertial methods for solving composite convex minimization problems. Since the early works of Polyak and Nesterov, inertial methods become very popular, thanks to their acceleration effects. Here, we study a family of Nesterov-type inertial proximalgradient algorithms with a particular over-relaxation sequence. We give a unified presentation about the different convergence properties of this family of algorithms, depending on the over-relaxation parameter. In addition we addressing this issue, in the case of a smooth function with additional geometrical structure, such as the growth (or Łojasiewicz) condition. We show that by combining growth condition and a flatness-type condition on the geometry of the minimizing function, we are able to obtain some new convergence rates. Our analysis follows a continuous-to-discrete trail, passing from continuous-on time-dynamical systems to discrete schemes. In particular the family of inertial algorithms that interest us, can be identified as a finite difference scheme of a differential equation/inclusion. This approach provides a useful guideline, which permits to transpose the different results and their proofs from the continuous system to the discrete one. This opens the way for new possible inertial schemes, derived by the same dynamical system.
4

Numerical splitting methods for nonsmooth convex optimization problems

Bitterlich, Sandy 11 December 2023 (has links)
In this thesis, we develop and investigate numerical methods for solving nonsmooth convex optimization problems in real Hilbert spaces. We construct algorithms, such that they handle the terms in the objective function and constraints of the minimization problems separately, which makes these methods simpler to compute. In the first part of the thesis, we extend the well known AMA method from Tseng to the Proximal AMA algorithm by introducing variable metrics in the subproblems of the primal-dual algorithm. For a special choice of metrics, the subproblems become proximal steps. Thus, for objectives in a lot of important applications, such as signal and image processing, machine learning or statistics, the iteration process consists of expressions in closed form that are easy to calculate. In the further course of the thesis, we intensify the investigation on this algorithm by considering and studying a dynamical system. Through explicit time discretization of this system, we obtain Proximal AMA. We show the existence and uniqueness of strong global solutions of the dynamical system and prove that its trajectories converge to the primal-dual solution of the considered optimization problem. In the last part of this thesis, we minimize a sum of finitely many nonsmooth convex functions (each can be composed by a linear operator) over a nonempty, closed and convex set by smoothing these functions. We consider a stochastic algorithm in which we take gradient steps of the smoothed functions (which are proximal steps if we smooth by Moreau envelope), and use a mirror map to 'mirror'' the iterates onto the feasible set. In applications, we compare them to similar methods and discuss the advantages and practical usability of these new algorithms.
5

Hydrodynamische Lyapunov-Moden in mehrkomponentigen Lennard-Jones-Flüssigkeiten

Drobniewski, Christian 22 March 2011 (has links) (PDF)
Die Charakterisierung hochdimensionaler Systeme mit Lyapunov-Instabilität wird durch das Lyapunov-Spektrum und die zugehörigen Lyapunov-Vektoren ermöglicht. Für eine Vielzahl von derartigen Systemen (Coupled-Map-Lattices, Hartkugel-Systeme, Systeme mit ausgedehnten Potentialen ...) konnte durch die Untersuchung der Lyapunov-Vektoren die Existenz von hydrodynamischen Lyapunov-Moden nachgewiesen werden. Diese kollektiven Anregungen zeigen sich in Lyapunov-Vektoren, deren Lyapunov-Exponenten dem Betrage nach am kleinsten sind. Da Lyapunov-Exponenten charakteristische Zeitskalen innerhalb der Systeme repräsentieren, ist durch die Lyapunov-Moden eine Untersuchung des Langzeitverhaltens möglich. In dieser Arbeit werden die hydrodynamischen Lyapunov-Moden durch Molekulardynamiksimulationen von mehrkomponentigen Lennard-Jones-Flüssigkeiten untersucht. Die Charakterisierung der Lyapunov-Moden zeigt im weiteren eine Ähnlichkeit zu Dispersionsrelationen von Phononen.
6

Dynamic stability control and human energetics

Ekizos, Antonis 13 November 2018 (has links)
Die Bewegungs-kontrollstrategien kontextabhängig und abhängig von unterschiedlichen Kriterien ausgewählt werden. Einerseits ist die Stabilität in den Bewegungszuständen wie der Fortbewegung ausschlaggebend für die ungestörte Ausführung bestimmter Handlungen und erfordert eine effektive Steuerung durch das zentrale Nervensystem. Andererseits wird die Bewegungsstrategieauswahl durch das zentrale Nervensystem dadurch bestimmt, dass die Energiekosten minimiert werden soll. Beide Konzepte (d.h. die Aufrechterhaltung der Stabilität und die Energiekostenminimierung) spielen eine fundamentale Rolle bei der Frage, warum sich Menschen so bewegen, wie sie es tun. Unklar ist dabei allerdings, auf welche Weise das zentrale Nervensystem beide Prinzipien gegeneinander gewichtet. In den letzten 20 Jahren haben uns wissenschaftliche Konzepte wie die Chaostheorie oder die Theorie komplexer Systeme eine neue Herangehensweise an diese Fragen ermöglicht. Diese Arbeit untersucht die dynamische Stabilität menschlicher Fortbewegung mit Hilfe des Konzepts der Ljapunowanalyse. Als erstes wird eine methodologische Untersuchung der Verlässlichkeit des maximalen Ljapunowexponenten beim Gehen und Laufen durchgeführt (Kapitel 2). Danach wird verglichen zwischen dem Laufen unter normalen Umständen und dem darauffolgenden Laufen ohne Schuhe, wobei letzteres eine Abnahme der Stabilität nach dem Übergang zu den neuen Umständen zur Folge hat (Kapitel 3). In der letzten Untersuchung wurde ein unterschiedlich langes Training zur Verbesserung der Laufenergetik durchgeführt, in einer Gruppe nur über einen kurzen und in einer anderen Gruppe über einen etwas längeren Zeitraum (Kapitel 4). Die Ergebnisse zeigen, dass Bewegungskontrollfehler für die Energiekosten beim Laufen eine Rolle spielen können, und legen somit eine flexible Priorisierung der Bewegungskontrolle nahe. / Motor control strategies are chosen in a context dependent manner, based on different criteria. On the one hand stability in dynamic conditions such as locomotion, is crucial to uninterrupted task execution and requires effective regulation by the central nervous system. On the other, minimization of the energetic cost of transport is instrumental in choosing the locomotion strategy by the central nervous system. Both these concepts, (i.e. maintaining stability and optimization of energetic cost of locomotion) have a fundamental role on how and why humans move in the way they do. However, how the human central nervous system prioritizes between the different goals is unknown. In the last 20 years, ideas from scientific paradigms such as chaos theory and complex systems have given us novel tools to approach these questions. The current thesis examines the dynamic stability during human locomotion under such an approach using the concept of Lyapunov analysis. At first a methodological examination of the reliability of the maximum Lyapunov exponent in walking and running has been conducted (chapter 2). Afterwards, an examination between the habitual running condition and after removal of footwear was conducted, exhibiting a decrease in stability following the acute transition to the new condition (chapter 3). In the last study, a training intervention aiming at improvements in running energetics was performed using a short-term and a long-term intervention group (chapter 4). The results evidence that motor control errors can have a role in the energy cost of running and thus, a flexible prioritization of the motor control output.
7

Contribution to adaptative sliding mode, fault tolerant control and control allocation of wind turbine system / Contribution à la commande par modes glissants adaptative et tolérantes aux défauts : Application au système éolien

Liu, Xinyi 25 November 2016 (has links)
Les principaux défis pour le déploiement de systèmes de conversion de l'énergie éolienne est de maximiser la puissance électrique produite, malgré les variations des conditions météorologiques, tout en minimisant les coûts de fabrication et de maintenance du système. L'efficacité de la turbine éolienne est fortement dépendante des perturbations de l'environnement et des paramètres variables du système, tels que la vitesse du vent et l'angle de tangage. Les incertitudes sur le système sont difficiles à modéliser avec précision alors qu'ils affectent sa stabilité.Afin d'assurer un état de fonctionnement optimal, malgré les perturbations, le commande adaptative peut jouer un rôle déterminant. D'autre part, la synthèse de commandes tolérantes aux défauts, capables de maintenir les éoliennes connectées au réseau après la survenance de certains défauts est indispensable pour le bon fonctionnement du réseau. Le travail de cette thèse porte sur la mise en place de lois de commande adaptatives et tolérantes aux défauts appliqués aux systèmes de conversion de l'énergie éolienne. Après un état de l'art, les contributions de la thèse sont :Dans la première partie de la thèse, un modèle incertain non linéaire du système de conversion d'énergie éolienne avec un générateur à induction à double alimentation est proposé. Une nouvelles approches de commande adaptative par mode glissant est synthétisée et ensuite appliquée pour optimiser l'énergie issue de l'éolienne.Dans la deuxième partie, une nouvelle commande par modes glissants tolérante aux défauts et basée sur les modes glissants intégrales est présentée. Puis, cette méthode est appliquée afin de forcer la vitesse de la turbine éolienne à sa valeur optimale en prenant en compte des défauts qui surviennent sur l'actionneur. / The main challenges for the deployment of wind energy conversion systems (WECS) are to maximize the amount of good quality electrical power extracted from wind energy over a significantly wide range of weather conditions and minimize both manufacturing and maintenance costs. Wind turbine's efficiency is highly dependent on environmental disturbances and varying parameters for operating conditions, such as wind speed, pitch angle, tip-speed ratio, sensitive resistor and inductance. Uncertainties on the system are hard to model exactly while it affects the stability of the system. In order to ensure an optimal operating condition, with unknown perturbations, adaptive control can play an important role. On the other hand, a Fault Tolerant Control (FTC) with control allocation that is able to maintain the WECS connected after the occurrence of certain faults can avoid major economic losses. The thesis work concerns the establishment of an adaptive control and fault diagnosis and tolerant control of WECS. After a literature review, the contributions of the thesis are:In the first part of the thesis, a nonlinear uncertain model of the wind energy conversion system with a doubly fed induction generator (DFIG) is proposed. A novel Lyapunov-based adaptive Sliding Mode (HOSM) controller is designed to optimize the generated power.In the second part, a new output integral sliding mode methodology for fault tolerant control with control allocation of linear time varying systems is presented. Then, this methodology has been applied in order to force the wind turbine speed to its optimal value the presence of faults in the actuator.
8

Hydrodynamische Lyapunov-Moden in mehrkomponentigen Lennard-Jones-Flüssigkeiten

Drobniewski, Christian 22 June 2010 (has links)
Die Charakterisierung hochdimensionaler Systeme mit Lyapunov-Instabilität wird durch das Lyapunov-Spektrum und die zugehörigen Lyapunov-Vektoren ermöglicht. Für eine Vielzahl von derartigen Systemen (Coupled-Map-Lattices, Hartkugel-Systeme, Systeme mit ausgedehnten Potentialen ...) konnte durch die Untersuchung der Lyapunov-Vektoren die Existenz von hydrodynamischen Lyapunov-Moden nachgewiesen werden. Diese kollektiven Anregungen zeigen sich in Lyapunov-Vektoren, deren Lyapunov-Exponenten dem Betrage nach am kleinsten sind. Da Lyapunov-Exponenten charakteristische Zeitskalen innerhalb der Systeme repräsentieren, ist durch die Lyapunov-Moden eine Untersuchung des Langzeitverhaltens möglich. In dieser Arbeit werden die hydrodynamischen Lyapunov-Moden durch Molekulardynamiksimulationen von mehrkomponentigen Lennard-Jones-Flüssigkeiten untersucht. Die Charakterisierung der Lyapunov-Moden zeigt im weiteren eine Ähnlichkeit zu Dispersionsrelationen von Phononen.

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