Magnetorheological (MR) dampers are adaptive devices whose properties can be adjusted through the application of a controlled voltage signal. A semi-active suspension system incorporating MR dampers combines the advantages of both active and passive suspensions. For this reason, there has been a continuous effort to develop control algorithms for MR-damped vehicle suspension systems to meet the requirements of the automotive industry. The overall aims of this thesis are twofold: (i) The investigation of non-parametric techniques for the identification of the nonlinear dynamics of an MR damper. (ii) The implementation of these techniques in the investigation of MR damper control of a vehicle suspension system that makes minimal use of sensors, thereby reducing the implementation cost and increasing system reliability. The novel contributions of this thesis can be listed as follows: 1- Nonparametric identification modelling of an MR damper using Chebyshev polynomials to identify the damping force from both simulated and experimental data. 2- The neural network identification of both the direct and inverse dynamics of an MR damper through an experimental procedure. 3- The experimental evaluation of a neural network MR damper controller relative to previously proposed controllers. 4- The application of the neural-based damper controller trained through experimental data to a semi-active vehicle suspension system. 5- The development and evaluation of an improved control strategy for a semi-active car seat suspension system using an MR damper. Simulated and experimental validation data tests show that Chebyshev polynomials can be used to identify the damper force as an approximate function of the displacement, velocity and input voltage. Feed-forward and recurrent neural networks are used to model both the direct and inverse dynamics of MR dampers. It is shown that these neural networks are superior to Chebyshev polynomials and can reliably represent both the direct and inverse dynamic behaviours of MR dampers. The neural network models are shown to be reasonably robust against significant temperature variation. Experimental tests show that an MR damper controller based a recurrent neural network (RNN) model of its inverse dynamics is superior to conventional controllers in achieving a desired damping force, apart from being more cost-effective. This is confirmed by introducing such a controller into a semi-active suspension, in conjunction with an overall system controller based on the sliding mode control algorithm. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road excitations. A study using the modified Bouc-Wen model for the MR damper, and another study using an actual damper fitted in a hardware-in-the-loop- simulation (HILS), both show that the inverse RNN damper controller potentially gives significantly superior ride comfort and vehicle stability. It is also shown that a similar control strategy is highly effective when used for a semi-active car seat suspension system incorporating an MR damper.

Dans la première partie de cette thèse, on s'intéresse à la suite NX(p) [mod p] où X est un schéma séparé réduit de type fini sur Z,et pour tout p premier, NX(p) est le nombre de Fp-points de la réduction modulo p de X.Sous certaines hypothèses sur la géométrie de X, on donne une condition simple pour garantir que cette suite diffèreen une densité positive de coordonnées de la suite identiquement nulle,ou plus généralement de suites dont les coordonnées sont obtenues par réduction modulo p d'un nombre fini d'entiers.Dans le cas où X parcourt une famille de courbes hyperelliptiques, on donne une borne en moyenne sur le plus petit premier p pour lequel NX (p) [mod p] n'est pas dans un certain ensemble de valeurs fixées.La seconde partie est dédiée à des généralisations de la notion de biais de Chebyshev.On se donne une fonction L vérifiant certaines propriétés analytiquesgénéralisant celles vérifiées par les fonctions L de Dirichlet.On s'intéresse à la suite des coefficients de Fourier a_p pour p premier.Plus précisément on étudie le signe de la fonction sommatoire des coefficients de Fourier de la fonction L.On montre sous des conditions classiques que cette fonction admet une distribution logarithmique limite.Sous des hypothèses supplémentaires on obtient de bonnes propriétés telles que la régularité, la symétrie et des informations sur le support de cette distribution. / In the first part of this Thesis, we study the sequence NX (p) [mod p] where X is a reduced separated scheme of finite type over Z,and NX (p) is the number of Fp-points of the reduction modulo p of X, for every prime p. Under some hypotheses on the geometry of X, we give a simple condition to ensure that this sequence is distinctat a positive proportion of indices from the zero sequence,or generalizations obtained by reduction modulo p of finitely many integers.We give a bound on average over a family of hyperelliptic curves for the least prime p such that NX (p) [mod p] avoids the reductionmodulo p of finitely many fixed integers.The second part deals with generalizations of Chebyshev’s bias.We consider an L-function satisfying some analytic properties that generalize those satisfied by Dirichlet L-functions.We study the sequence of coefficients a_p as p runs through the set of prime numbers.Precisely, we study the sign of the summatory function of the Fourier coefficients of the L-function.Under some classical conditions, we show that this function admits a limiting logarithmic distribution.Under stronger hypotheses, we prove regularity, symmetry and get information about the support of this distribution.

The thesis is focused on the investigation of Rayleigh-Bénard problem in an extended setting approximating the conditions in the Earth's mantle. The aim is to evaluate the influence of depth- and temperature- dependent material parameters, dissipation, adiabatic heating/cooling and heat sources on the qualitative characteristics of thermal convection. We identify the critical values of dimensionless parameters that determine the onset of convection and characterize the dominating convection patterns in marginally supercritical states. These issues are addressed by the application of linear stability analysis and weakly non-linear analysis. It has been found that the character of convection differ substantially from the standard case of Rayleigh-Bénard convection. Powered by TCPDF (www.tcpdf.org)

In this work, the fast evaluation and reconstruction of multivariate trigonometric polynomials with frequencies supported on arbitrary index sets of finite cardinality is considered, where rank-1 lattices are used as spatial discretizations. The approximation of multivariate smooth periodic functions by trigonometric polynomials is studied, based on a one-dimensional FFT applied to function samples. The smoothness of the functions is characterized via the decay of their Fourier coefficients, and various estimates for sampling errors are shown, complemented by numerical tests for up to 25 dimensions. In addition, the special case of perturbed rank-1 lattice nodes is considered, and a fast Taylor expansion based approximation method is developed. One main contribution is the transfer of the methods to the non-periodic case. Multivariate algebraic polynomials in Chebyshev form are used as ansatz functions and rank-1 Chebyshev lattices as spatial discretizations. This strategy allows for using fast algorithms based on a one-dimensional DCT. The smoothness of a function can be characterized via the decay of its Chebyshev coefficients. From this point of view, estimates for sampling errors are shown as well as numerical tests for up to 25 dimensions. A further main contribution is the development of a high-dimensional sparse FFT method based on rank-1 lattice sampling, which allows for determining unknown frequency locations belonging to the approximately largest Fourier or Chebyshev coefficients of a function. / In dieser Arbeit wird die schnelle Auswertung und Rekonstruktion multivariater trigonometrischer Polynome mit Frequenzen aus beliebigen Indexmengen endlicher Kardinalität betrachtet, wobei Rang-1-Gitter (rank-1 lattices) als Diskretisierung im Ortsbereich verwendet werden. Die Approximation multivariater glatter periodischer Funktionen durch trigonometrische Polynome wird untersucht, wobei Approximanten mittels einer eindimensionalen FFT (schnellen Fourier-Transformation) angewandt auf Funktionswerte ermittelt werden. Die Glattheit von Funktionen wird durch den Abfall ihrer Fourier-Koeffizienten charakterisiert und mehrere Abschätzungen für den Abtastfehler werden gezeigt, ergänzt durch numerische Tests für bis zu 25 Raumdimensionen. Zusätzlich wird der Spezialfall gestörter Rang-1-Gitter-Knoten betrachtet, und es wird eine schnelle Approximationsmethode basierend auf Taylorentwicklung vorgestellt. Ein wichtiger Beitrag dieser Arbeit ist die Übertragung der Methoden vom periodischen auf den nicht-periodischen Fall. Multivariate algebraische Polynome in Chebyshev-Form werden als Ansatzfunktionen verwendet und sogenannte Rang-1-Chebyshev-Gitter als Diskretisierungen im Ortsbereich. Diese Strategie ermöglicht die Verwendung schneller Algorithmen basierend auf einer eindimensionalen DCT (diskreten Kosinustransformation). Die Glattheit von Funktionen kann durch den Abfall ihrer Chebyshev-Koeffizienten charakterisiert werden. Unter diesem Gesichtspunkt werden Abschätzungen für Abtastfehler gezeigt sowie numerische Tests für bis zu 25 Raumdimensionen. Ein weiterer wichtiger Beitrag ist die Entwicklung einer Methode zur Berechnung einer hochdimensionalen dünnbesetzten FFT basierend auf Abtastwerten an Rang-1-Gittern, wobei diese Methode die Bestimmung unbekannter Frequenzen ermöglicht, welche zu den näherungsweise größten Fourier- oder Chebyshev-Koeffizienten einer Funktion gehören.

This thesis presents a numerical approach based on generalized fractional-order Chebyshev wavelets for solving fractional-order optimal control problems. The exact value of the Riemann– Liouville fractional integral operator of the generalized fractional-order Chebyshev wavelets is computed by applying the regularized beta function. We apply the given wavelets, the exact formula, and the collocation method to transform the studied problem into a new optimization problem. The convergence analysis of the proposed method is provided. The present method is extended for solving fractional-order, distributed-order, and variable-order optimal control problems. Illustrative examples are considered to show the advantage of this method in comparison with the existing methods in the literature.

Quand on veut évaluer ou manipuler une fonction mathématique f, il est fréquent de la remplacer par une approximation polynomiale p. On le fait, par exemple, pour implanter des fonctions élémentaires en machine, pour la quadrature ou la résolution d'équations différentielles ordinaires (ODE). De nombreuses méthodes numériques existent pour l'ensemble de ces questions et nous nous proposons de les aborder dans le cadre du calcul rigoureux, au sein duquel on exige des garanties sur la précision des résultats, tant pour l'erreur de méthode que l'erreur d'arrondi.Une approximation polynomiale rigoureuse (RPA) pour une fonction f définie sur un intervalle [a,b], est un couple (P, Delta) formé par un polynôme P et un intervalle Delta, tel que f(x)-P(x) appartienne à Delta pour tout x dans [a,b].Dans ce travail, nous analysons et introduisons plusieurs procédés de calcul de RPAs dans le cas de fonctions univariées. Nous analysons et raffinons une approche existante à base de développements de Taylor.Puis nous les remplaçons par des approximants plus fins, tels que les polynômes minimax, les séries tronquées de Chebyshev ou les interpolants de Chebyshev.Nous présentons aussi plusieurs applications: une relative à l'implantation de fonctions standard dans une bibliothèque mathématique (libm), une portant sur le calcul de développements tronqués en séries de Chebyshev de solutions d'ODE linéaires à coefficients polynômiaux et, enfin, un processus automatique d'évaluation de fonction à précision garantie sur une puce reconfigurable.

In dieser Dissertation werden algorithmische Verbesserungen und Varianten für Simulationen der zwei-Flavor Gitter QCD mit dynamischen Fermionen studiert. Der O(a)-verbesserte Dirac-Wilson-Operator wird im Schrödinger Funktional mit einem Update des Hybrid Monte Carlo (HMC)-Typs verwendet. Sowohl der Hermitische als auch der nicht-Hermitische Operator werden betrachtet. Für den Hermitischen Dirac-Wilson-Operator untersuchen wir die Vorteile des symmetrischen gegenüber dem asymmetrischen Gerade-Ungerade-Präkonditionierens, wie man von einem mehr Zeitskalen-Integrator profitieren kann, sowie die Auswirkungen der kleinsten Eigenwerte auf die Stabilität des HMC Algorithmus. Im Fall des nicht-Hermitischen Operators leiten wir eine (semi)-analytische Schranke für das Spektrum her und zeigen eine Methode, um Informationen über den spektralen Rand zu gewinnen, indem wir komplexe Eigenwerte mit dem Lanczos-Algorithmus abschätzen. Diese spektralen Ränder erlauben es, Vorzüge des symmetrischen Gerade-Ungerade-Präkonditionierens oder den Effekt des Sheikholeslami-Wohlert-Terms für das Spektrum des nicht-Hermitischen Operators zu zeigen. Unter Verwendung der Informationen des spektralen Randes konstruieren wir angepasste, komplexe, skalierte und verschobene Tschebyschow Polynome zur Approximation des inversen Dirac-Wilson-Operators. Basierend auf diesen Polynomen entwickeln wir eine neue HMC-Variante, genannt nicht-Hermitischer polynomialer Hybrid Monte Carlo (NPHMC). Sie erlaubt, vom Importance Sampling unter Kompensation mit einem Gewichtungsfaktor abzuweichen. Zudem wird eine Erweiterung durch Anwendung des Hasenbusch-Tricks abgeleitet. Erste Größen der Leistungsfähigkeit, die die Abhängingkeit von den Eingabeparametern als auch einen Vergleich mit unserem Standard-HMC zeigen, werden präsentiert. Im Vergleich der beiden ein-Pseudofermion-Varianten ist der neue NPHMC etwas besser; eine eindeutige Aussage im Fall der zwei-Pseudofermion-Variante ist noch nicht möglich. / In this thesis algorithmic improvements and variants for two-flavor lattice QCD simulations with dynamical fermions are studied using the O(a)-improved Dirac-Wilson operator in the Schrödinger functional setup and employing a hybrid Monte Carlo-type (HMC) update. Both, the Hermitian and the Non-Hermitian operator are considered. For the Hermitian Dirac-Wilson operator we investigate the advantages of symmetric over asymmetric even-odd preconditioning, how to gain from multiple time scale integration as well as how the smallest eigenvalues affect the stability of the HMC algorithm. In case of the non-Hermitian operator we first derive (semi-)analytical bounds on the spectrum before demonstrating a method to obtain information on the spectral boundary by estimating complex eigenvalues with the Lanzcos algorithm. These spectral boundaries allow to visualize the advantage of symmetric even-odd preconditioning or the effect of the Sheikholeslami-Wohlert term on the spectrum of the non-Hermitian Dirac-Wilson operator. Taking advantage of the information of the spectral boundary we design best-suited, complex, scaled and translated Chebyshev polynomials to approximate the inverse Dirac-Wilson operator. Based on these polynomials we derive a new HMC variant, named non-Hermitian polynomial Hybrid Monte Carlo (NPHMC), which allows to deviate from importance sampling by compensation with a reweighting factor. Furthermore an extension employing the Hasenbusch-trick is derived. First performance figures showing the dependence on the input parameters as well as a comparison to our standard HMC are given. Comparing both algorithms with one pseudo-fermion, we find the new NPHMC to be slightly superior, whereas a clear statement for the two pseudo-fermion variants is yet not possible.

Submitted by Luciclea Silva (luci@ufpa.br) on 2018-09-25T17:40:10Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_Perfomanceevaluationrobust.pdf: 5317740 bytes, checksum: 4bd1f1a3fd9e888155a633221aa5e096 (MD5) / Approved for entry into archive by Luciclea Silva (luci@ufpa.br) on 2018-09-25T17:40:47Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_Perfomanceevaluationrobust.pdf: 5317740 bytes, checksum: 4bd1f1a3fd9e888155a633221aa5e096 (MD5) / Made available in DSpace on 2018-09-25T17:40:47Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertacao_Perfomanceevaluationrobust.pdf: 5317740 bytes, checksum: 4bd1f1a3fd9e888155a633221aa5e096 (MD5) Previous issue date: 2018-06-11 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Conversores chaveados são sistemas amplamente utilizadas em aplicações industriais. Tais sistemas operam via controle em malha fechada e, dessa forma, os aspectos de estabilidade e desempenho em malha fechada devem ser assegurados pelo projetista. Recentemente, o emprego de sistemas com múltiplos conversores vem se tornando comum em uma ampla gama de aplicações. A interação entre os sistemas de controle os múltiplos conversores pode levar o sistema a operar com reduzidas margens de estabilidade ou com um baixo desempenho dinâmico. Portanto, a estabilidade de um sistema com conversores operando em cascata é uma grande preocupação para aplicações reais. A instabilidade em sistemas em cascata pode ocorrer devido à carga de potência constante (CPL), que é um comportamento dos conversores quando se encontram regulados. As CPLs exibem comportamento de resistência negativa incremental, causando um alto risco de instabilidade em conversores interligados. Dessa forma, a mitigação desse problema é uma questão importante no projeto da fonte de alimentação comutada de múltiplos estágios, de modo a garantir a estabilidade de todo o sistema. No entanto, algumas dificuldades estão presentes além da CPL, por exemplo, não linearidades fortes, devido à presença do elemento indutivo, além das incertezas em relação aos valores nominais dos componentes discretos que compõem o sistema. Neste trabalho é realizado um estudo experimental do desempenho das metodologias de controle robusto paramétrico aplicadas ao problema de mitigar o efeito adverso de cargas do tipo CPL, em um sistema com dois conversores buck operando em cascata (sistema buck-buck). Vários testes foram desenvolvidos utilizando tanto uma planta experimental quanto via simulação computacional em Matlab/ Simulink, quando o sistema multiconversor buck-buck é submetido a uma variação de potência. Os resultados mostram o melhor desempenho das metodologias propostas. / Multi-converter electronic systems are becoming widely used in many industrial applications; therefore, the stability of the cascaded system is a big concern to real-world power supplies applications. Instability in cascaded systems may occur due to the constant power load (CPL), which is a behavior of the tightly regulated converters. CPLs exhibit incremental negative resistance behavior causing a high risk of instability in interconnected converters; therefore, the mitigation of this problem is an important issue in the multiple-stage switched mode power supply design. Thus, it is important to guarantee stability of the whole system. However, some difficulties remains besides the CPL, e.g., non-linearities due to the inductive element and uncertainties due to imprecision of mathematical models and/or variation of nominal values of the discrete elements that compose the DC/DC buck converter. Aiming to evaluate the performance of the proposed robust methodologies in this work to mitigate the instability problem caused by a CPL, several tests were developed by using an experimental plant and Matlab/Simulink, when the multi-converter buck-buck system is subjected a variation of power reference. The results show the improved performance of the proposed methodologies.

<p>Diffusion is a phenomenon in which particles move from regions of higher density to regions of lower density. Many physical systems, in fields as diverse as plant biology and finance, are known to involve diffusion phenomena. Typically, diffusion systems are modeled by partial differential equations (PDEs), which include certain parameters. These parameters characterize a given diffusion system. Therefore, for both modeling and simulation of a diffusion system, one has to either know or determine these parameters. Moreover, as PDEs are infinite order dynamic systems, for computational purposes one has to approximate them by a finite order model. In this thesis, we investigate these two issues of model reduction and parameter estimation by considering certain specific cases of heat diffusion systems. </p><p>We first address model reduction by considering two specific cases of heat diffusion systems. The first case is a one-dimensional heat diffusion across a homogeneous wall, and the second case is a two-dimensional heat diffusion across a homogeneous rectangular plate. In the one-dimensional case we construct finite order approximations by using some well known PDE solvers and evaluate their effectiveness in approximating the true system. We also construct certain other alternative approximations for the one-dimensional diffusion system by exploiting the different modal structures inherently present in it. For the two-dimensional heat diffusion system, we construct finite order approximations first using the standard finite difference approximation (FD) scheme, and then refine the FD approximation by using its asymptotic limit.</p><p>As for parameter estimation, we consider the same one-dimensional heat diffusion system, as in model reduction. We estimate the parameters involved, first using the standard batch estimation technique. The convergence of the estimates are investigated both numerically and theoretically. We also estimate the parameters of the one-dimensional heat diffusion system recursively, initially by adopting the standard recursive prediction error method (RPEM), and later by using two different recursive algorithms devised in the frequency domain. The convergence of the frequency domain recursive estimates is also investigated. </p>

The technique of sensor fusion addresses the issues relating to the optimality of decision-making in the multiple-sensor framework. The advances in sensor fusion enable to perform intrusion detection for both rare and new attacks. This thesis discusses this assertion in detail, and describes the theoretical and experimental work done to show its validity. The attack-detector relationship is initially modeled and validated to understand the detection scenario. The different metrics available for the evaluation of intrusion detection systems are also introduced. The usefulness of the data set used for experimental evaluation has been demonstrated. The issues connected with intrusion detection systems are analyzed and the need for incorporating multiple detectors and their fusion is established in this work. Sensor fusion provides advantages with respect to reliability and completeness, in addition to intuitive and meaningful results. The goal for this work is to investigate how to combine data from diverse intrusion detection systems in order to improve the detection rate and reduce the false-alarm rate. The primary objective of the proposed thesis work is to develop a theoretical and practical basis for enhancing the performance of intrusion detection systems using advances in sensor fusion with easily available intrusion detection systems. This thesis introduces the mathematical basis for sensor fusion in order to provide enough support for the acceptability of sensor fusion in performance enhancement of intrusion detection systems. The thesis also shows the practical feasibility of performance enhancement using advances in sensor fusion and discusses various sensor fusion algorithms, its characteristics and related design and implementation is-sues. We show that it is possible to build performance enhancement to intrusion detection systems by setting proper threshold bounds and also by rule-based fusion. We introduce an architecture called the data-dependent decision fusion as a framework for building intrusion detection systems using sensor fusion based on data-dependency. Furthermore, we provide information about the types of data, the data skewness problems and the most effective algorithm in detecting different types of attacks. This thesis also proposes and incorporates a modified evidence theory for the fusion unit, which performs very well for the intrusion detection application. The future improvements in individual IDSs can also be easily incorporated in this technique in order to obtain better detection capabilities. Experimental evaluation shows that the proposed methods have the capability of detecting a significant percentage of rare and new attacks. The improved performance of the IDS using the algorithms that has been developed in this thesis, if deployed fully would contribute to an enormous reduction of the successful attacks over a period of time. This has been demonstrated in the thesis and is a right step towards making the cyber space safer.