La réalisation de tâches de manipulation dextres requiert une complexité aussi bien dans la conception de préhenseur robotique que dans la synthèse de leurs lois de commande. Une optimisation de la mécatronique de ces systèmes permet de répondre aux contraintes d'intégration fonctionnelle en se passant de capteurs de force terminaux. L'utilisation de mécanismes réversibles rend alors possible la détermination du positionnement du système dans l'espace libre et la détection de son interaction avec les objets manipulés, grâce aux mesures proprioceptives inhérentes aux actionneurs électriques. L'objectif de cette thèse est de parvenir synthétiser, dans le contexte articulaire (un degré-de-liberté), une commande adaptée à la manipulation en tenant compte de ces particularités mécaniques. La méthode proposée est basée sur une commande robuste par rapport aux non-linéarités structurelles dues aux effets gravitationnels et aux frottements secs d'une part et par rapport aux rigidités variables des objets manipulés. L'approche choisie nécessite la connaissance précise de la configuration du système étudié à chaque instant. Une représentation dynamique de son comportement permet de synthétiser un capteur logiciel pour l'estimation des grandeurs indispensables à la commande. Ces différentes étapes sont validées par des essais expérimentaux pour justifier la démarche choisie menant à une commande adaptée à la manipulation d'objets. / The realization of dexterous manipulation tasks requires a complexity in robotic hands design as well as in their control laws synthesis. A mecatronical optimization of these systems helps to answer for functional integration constraints by avoiding external force sensors. Back-drivable mechanics allows the free-space positioning determination of such system as far as the detection of its interaction with a manipulated object thanks to proprioceptives measures at electric actuator level. The objective of this thesis is to synthesize a control law adapted to object manipulation by taking into account these mechanical properties in a one degree-of-freedom case. The proposed method is based on a robust control according to structural non-linearities due to gravitational effects and dry frictions on the one hand and with regard to a variable rigidity of manipulated objects on the other hand. The chosen approach requires a precise knowledge of the system configuration at all time. A dynamic representation of its behavior enables a software sensor synthesis for the exteroceptives variables estimation in a control law application purpose. The different steps are experimentally validated in order to justify the chosen approach leading to object manipulation.

Les réseaux de lois de bilan sont définis par l'interconnexion, via des conditions aux bords, de modules élémentaires individuellement caractérisés par la conservation de certaines quantités. Des applications industrielles se trouvent dans les réseaux de lignes de transmission électriques (réseaux HVDC), hydrauliques et pneumatiques (réseaux de distribution du gaz, de l'eau et du fuel). La thèse se concentre sur l'analyse modulaire et la commande au bord d'une ligne élémentaire représentée par un système de lois de bilan en dimension infinie, où la dynamique de la ligne est prise en considération au moyen d'équations aux dérivées partielles hyperboliques linéaires du premier ordre et couplées deux à deux. Cette dynamique permet de modéliser d'une manière rigoureuse les phénomènes de transport et les vitesses finies de propagation, aspects normalement négligés dans le régime transitoire. Les développements de ces travaux sont des outils d'analyse qui testent la stabilité du système, et de commande au bord pour la stabilisation autour d'un point d'équilibre. Dans la partie analyse, nous considérons un système de lois de bilan avec des couplages statiques aux bords et anti-diagonaux à l’intérieur du domaine. Nous proposons des conditions suffisantes de stabilité, tant explicites en termes des coefficients du système, que numériques par la construction d'un algorithme. La méthode se base sur la reformulation du problème en une analyse, dans le domaine fréquentiel, d'un système à retard obtenu en appliquant une transformation backstepping au système de départ. Dans le travail de stabilisation, un couplage avec des dynamiques décrites par des équations différentielles ordinaires (EDO) aux deux bords des EDP est considéré. Nous développons une transformation backstepping (bornée et inversible) et une loi de commande qui, à la fois stabilise les EDP à l'intérieur du domaine et la dynamique des EDO, et élimine les couplages qui peuvent potentiellement mener à l’instabilité. L'efficacité de la loi de commande est illustrée par une simulation numérique. / Networks of balance laws are defined by the interconnection, via boundary conditions, of elementary modules individually characterized by the conservation of physical quantities. Industrial applications of such networks can be found in electric (HVDC networks), hydraulic and pneumatic (gas, water and oil distribution) transmission lines. The thesis is focused on modular analysis and boundary control of an elementary line represented by a system of balance laws in infinite dimension, where the dynamics of the line is taken into consideration by means of first order two by two coupled linear hyperbolic partial differential equations. This representation allows to rigorously model the transport phenomena and finite propagation speed, aspects usually neglected in transient regime. The developments of this work are analysis tools that test the stability, as well as boundary control for the stabilization around an equilibrium point. In the analysis section, we consider a system of balance laws with static boundary conditions and anti-diagonal in-domain couplings. We propose sufficient stability conditions, explicit in terms of the system coefficients, and numerical by constructing an algorithm. The method is based on reformulating the analysis problem as an analysis of a delay system in the frequency domain, obtained by applying a backstepping transform to the original system. In the stabilization work, couplings with dynamic boundary conditions, described by ordinary differential equations (ODE), at both boundaries of the PDEs are considered. We develop a backstepping (bounded and invertible) transform and a control law that at the same time, stabilizes the PDEs inside the domain and the ODE dynamics, and eliminates the couplings that are a potential source of instability. The effectiveness of the control law is illustrated by a numerical simulation.

First, basic aspects of interval analysis, roles of intervals and their applications are addressed. Then, various classes of interval matrices are described and their relations are depicted. This material forms a prelude to the unifying theme of the rest of the work - solving interval linear systems. Several methods for enclosing the solution set of square and overdetermined interval linear systems are covered and compared. For square systems the new shaving method is introduced, for overdetermined systems the new subsquares approach is introduced. Detecting unsolvability and solvability of such systems is discussed and several polynomial conditions are compared. Two strongest condi- tions are proved to be equivalent under certain assumption. Solving of interval linear systems is used to approach other problems in the rest of the work. Computing enclosures of determinants of interval matrices is addressed. NP- hardness of both relative and absolute approximation is proved. New method based on solving square interval linear systems and Cramer's rule is designed. Various classes of matrices with polynomially computable bounds on determinant are characterized. Solving of interval linear systems is also used to compute the least squares linear and nonlinear interval regression. It is then applied to real...

[pt] Atuadores baseados em materiais piezelétricos apresentam características ideais para aplicações como transmissão acústica e micromanipulação. No entanto, não-linearidades inerentes a estes atuadores, como histerese e fluência, aumentam o desafio de controla-los. Além disso, a crescente necessidade de atuadores mais precisos e rápidos aliada a frequentes mudanças nas condições ambientais e operacionais agravam ainda mais o problema. Modelagens analíticas são específicas ao sistema ao qual foram feitas, o que significa que elas não são facilmente escalonáveis e eficientes para todos os tipos de sistemas. Adicionalmente, com o aumento da complexidade, os fenômenos que regem a física do sistema não são totalmente conhecidos, tornando difícil o desenvolvimento destes modelos. Este trabalho investiga esses desafios do ponto de vista da metodologia de identificação de sistemas e modelos baseados em dados para atuadores piezelétricos. A abordagem de modelagem caixa preta foi testada com dados experimentais adquiridos em um ambiente de laboratório para os estudos de caso de micromanipulação e transmissão acústica. Sinais de uso geral foram empregados como entrada de excitação do sistema de modo a acelerar a aquisição e estimação dos parâmetros. Parte dos modelos desenvolvidos foram validados com um conjunto de dados separado. Em ambos os casos foi necessário pré-processamento para otimização da quantidade de dados. Os modelos testados incluem a Média Móvel AutoRegressiva com entradas eXógenas (ARMAX), AutoRegressiva Não Linear com entradas eXógenas (NARX) com uma estrutura de rede neural artificial e Média Móvel AutoRegressiva Não Linear com entradas eXógenas (NARMAX). Os resultados mostram uma boa capacidade de prever as não-linearidades do micro manipulador e, portanto, a histerese em diferentes frequências de entrada. O sistema de transmissão acústica foi modelado com sucesso. Embora os resultados mostrem que ainda há espaço para melhorias, eles fornecem informações importantes sobre possíveis otimizações para o sistema uma vez que os modelos apresentados são uteis para janelas de predição curtas. / [en] Actuators based on piezoelectric materials have ideal characteristics for applications such as acoustic transmission and micromanipulation. However, the inherent nonlinearities of those actuators, such as hysteresis and creep, greatly increase the challenge to control such devices. Furthermore, the increasing need for more precise and faster actuators, allied with frequent changes in the environmental and operational conditions, further worsens the problem. Analytical models are application-specific, meaning that they are not easily and efficiently scalable to all systems. Also, with increased complexity, the understating of underlying phenomena is not fully documented, making it difficult to develop such models. This work investigates those challenges from the perspective of the system identification methodology and data-driven models for piezoelectric actuators. The black-box approach is tested with experimental data acquired in a laboratory setting for micromanipulator and acoustic transmission case studies. In some datasets, general-purpose signals were employed as the excitation input of the system to accelerate the data acquisition of the whole system dynamic and estimation process. Additionally, some models were validated on a separate dataset. In both cases, preprocessing was employed to optimize the amount of data. The tested models include the AutoRegressive Moving Average with eXogenous inputs (ARMAX), Nonlinear AutoRegressive with eXogenous inputs (NARX) with an artificial neural network structure, and Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX). The results show a good ability to predict the nonlinearities of the micromanipulator and, therefore, the hysteresis at different input frequencies. The acoustic transmission system was successfully modeled. Although the results show that there is still room for improvements, it provides insights into possible optimizations for the setup as the models here devised are useful for short prediction windows.

No description available.

[pt] Companhias de petróleo investindo no desenvolvimento de campos de hidrocarboneto dependem de estudos de reservatórios para realizarem previsões de produção e quantificarem os riscos associados à economicidade dos projetos. Neste sentido, a área de modelagem de reservatórios é de suma importância, sendo responsável por prever o desempenho futuro do reservatório sob diversas condições operacionais. Considerando que a solução dos sistemas de equações construídos a cada passo de tempo de uma simulação, durante o ciclo de linearização, é a parte que apresenta a maior demanda computacional, esta dissertação foca na análise de diferentes técnicas de solvers numéricos que podem ser aplicadas a simuladores, para mensurar seus desempenhos. Os solvers numéricos mais adequados para a solução de grandes sistemas de equações, tais como os encontrados em simulações de reservatórios, são os denominados solvers iterativos, que gradativamente aproximam a solução de um dado problema por meio da combinação de um método iterativo e um precondicionador. Os métodos iterativos avaliados nesta pesquisa foram o Gradiente Biconjugado Estabilizado (BiCGSTAB), Mínimos Resíduos Generalizado (GMRES) e Minimização Ortogonal (ORTHOMIN). Além disso, três técnicas de precondicionamento foram implementadas para auxiliar os métodos iterativos, sendo estas a Decomposição LU Incompleta (ILU), Fatoração Aninhada (NF) e Pressão Residual Restrita (CPR). A combinação destes diferentes métodos iterativos e precondicionadores permite a avaliação de diversas configurações distintas de solvers, em termos de seus desempenhos em um simulador. Os testes numéricos conduzidos neste trabalho utilizaram um novo simulador de reservatórios que está sendo desenvolvido pela Pontifícia Universidade Católica (PUC-Rio) em conjunto com a Petrobras. O objetivo dos testes foi analisar a robustez e eficiência de cada um dos solvers quanto à sua capacidade de resolver as equações de escoamento multifásico no meio poroso, visando assim auxiliar na seleção do solver mais adequado para o simulador. / [en] Petroleum companies investing in the development of hydrocarbon fields rely upon a variety of reservoir studies to perform production forecasts and quantify the risks associated with the economics of their projects. Integral to these studies is the discipline of reservoir modeling, responsible for predicting future reservoir performance under various operational conditions. Considering that the most time-demanding aspect of reservoir simulations is the solution of the systems of equations that arise within the linearization cycles at each time-step, this research focuses on analyzing different numerical solver techniques to be applied to a simulator, in order to assess their performance. The numerical solvers most suited for the solution of very large systems of equations, such as those encountered in reservoir simulations, are the so-called iterative solvers, which gradually approach the solution to a problem by combining an iterative strategy with a preconditioning method. The iterative methods examined in this research were the Stabilized Biconjugate Gradient (BiCGSTAB), the Generalized Minimum Residual (GMRES), and the Orthogonal Minimization (ORTHOMIN) methods. Furthermore, three preconditioning techniques were implemented to aid the iterative methods, namely the Incomplete LU Factorization (ILU), the Nested Factorization (NF), and the Constrained Pressure Residual (CPR) methods. The combination of these different iterative methods and preconditioners enables the appraisal of several distinct solver configurations, in terms of their performance in a simulator. The numerical tests conducted in this work made use of a new reservoir simulator currently under development at Pontifical Catholic University of Rio de Janeiro (PUC-Rio), as part of a joint project with Petrobras. The objective of these tests was to assess the robustness and efficiency of each solver in the solution of the multiphase flow equations in porous media, and support the selection of the solver most suited for the simulator.

Afin de limiter les contraintes dans les matériaux pour accroître leur durée de vie et améliorer la sécurité des structures (par exemple dans les transports), ainsi que d’améliorer le confort des utilisateurs, le contrôle de vibrations mécaniques et leur amortissement a fait l’objet de nombreuses recherche scientifiques depuis de nombreuses décennies. De plus, la prolifération récente des matériaux dits « intelligents » couplant plusieurs disciplines de la physique telles que la mécanique et l’électricité a permis l’élaboration de techniques de contrôle de vibration fiables, robustes et performantes tout en étant très intégrables, permettant ainsi de disposer de méthodes totalement adaptées aux système embarqués ou aux structures où les contraintes d’encombrement sont relativement restrictives. Notamment, il a récemment été proposé l’utilisation de techniques non linéaires basées sur une commutation synchronisée d’éléments piézoélectriques sur une impédance afin d’amélioration la conversion d’énergie mécanique sous forme électrique et ainsi de disposer de systèmes de contrôle de vibrations très performants et intégrables. Néanmoins, du fait du principe de cette commutation synchronisée avec la déformation, le contrôle de vibrations large bande, très présents dans les environnements réels, conduit à une dégradation des performances de ces techniques. L’objectif des travaux rapportés dans cette thèse consiste à proposer et à étudier théoriquement et expérimentalement des approches dérivées de ces techniques mais totalement adaptées au large bande. Ainsi, après une introduction relatant l’état de l’art en termes de contrôle vibratoire, la première technique exposée dans cette thèse propose d’utiliser un filtrage spatial permettant de séparer les modes de vibrations pour ensuite connecter de manière appropriée des éléments piézoélectriques afin de pouvoir simultanément contrôler plusieurs modes de vibrations en flexion. La deuxième méthode pour disposer de systèmes de contrôle de vibrations efficaces se base sur la combinaison d’amortisseurs à masse accordée avec l’approche non-linéaire afin d’améliorer le pouvoir d’amortissement par un contrôle supplémentaire des transferts énergétiques via le couplage électromécanique, conduisant à une méthode efficace, robuste et pouvant être installée facilement. La troisième et dernière approche consiste à utiliser les propriétés remarquables des structures périodiques en les couplant avec l’approche non-linéaire, cette dernière permettant une augmentation de l’amortissement et un élargissement significatif des bandes fréquentielles réduisant significativement l’amplitude de l’onde. Enfin, une conclusion générale exposera les principaux résultats obtenus et proposera des pistes d’évolution des concepts exposés. / In order to protect structures, extend their lifespan and decrease the incomfort resulting from undesired vibrations, many works have been reported for reducing vibrations. Along with the development of smart materials such as piezoelectric materials which are extensively used for vibration control and noise reduction due to their unique features (high integrability, compactness, light weight and high bandwidth), control systems can be designed in a more compact and simple form. Additionally, due to the conversion between mechanical energy and electrical energy, vibrations can be effectively attenuated by electromechanical approaches. Synchronized Switch Damping on Inductor (SSDI) technique attracted lot of attentions as an effective semi-passive technique which can artificially increase the converted energy by nonlinear voltage inversion process, thus allowing superior control performance compared to passive technique with low power requirement and simple control algorithm. Based on this semi-passive control technique, the objectives of this work are threefold. The first aim is improving the multimodal/broadband control performance of SSDI. An enhanced strategy based on spatial filtering according to the mode shapes of the vibrating structure is proposed. In order to separate the uninterested modes and effectively damp the targeted modes, sum and different switches respectively based on the sum of the piezovoltages of two anti-symmetrically bonded patches and the voltage difference of the two symmetrically bonded piezoelectric elements are introduced. Since the vibration modes can be spatially filtered by these connections, multimodal vibrations can be damped significantly and simultaneously as the sum and difference switches are employed, with an increase of total inversion coefficient. Then, electromechanical TMD (tuned mass damper) featuring piezoelectric materials combined with the semi-passive nonlinear technique SSDI is presented. Using this electromechanical semi-passive nonlinear TMD, the mechanical energy is not only transferred between host structure and TMD device but also converted as electrical energy stored in the piezoelectric patches and/or dissipated in the connected circuit, which allows excellent damping performance for limiting the vibrations. The last investigated method consists in electromechanical periodic structures featuring the nonlinear switching interface. Such a structure can effectively attenuate the elastic waves and damp the vibration in a wider frequency band since it has the capability of filtering propagative waves within stop bands attributed to the structural periodicity and the superior damping ability which is attributed to the nonlinear voltage inversion process that increases the voltage amplitude and decreases the phase between voltage and speed. Finally, a conclusion proposes a summary of the main results obtained in this thesis, as well as new extensions and ways of the proposed techniques.

<p>Predmet istraživanja je identifikaciji i adaptivna estimacija<br />parametara široke klase linearnih sistema. Predloženi algoritmi<br />za adaptivnu estimaciju parametara su primenjivi na sisteme koji se<br />opisuju funkcijama prenosa proizvoljnog oblika, što uključuje sisteme<br />sa kašnjenjem, distribuiranim parametrima, frakcione sisteme i<br />druge sisteme opisane iracionalnim funkcijama prenosa. Na<br />posletku, dat je algoritam za identifikaciju CNG sistema koji se ne<br />izvršava u realnom vremenu i pretpostavlja da struktura modela nije<br />poznata unapred.</p> / <p>The subject of this research is the system identification and adaptive<br />parameter estimation in wide class of linear processes. Proposed<br />approaches for adaptive parameter estimation can be applied to systems<br />described by transfer functions of arbitrary form, including systems with<br />delay, distributed-paratemeter systems, fractional order systems, and other<br />system described by irrational transfer functions. In the final part, an offline<br />algorithm for identification of CNG system which does not assume any a<br />priori known model structure is proposed.</p>

<p>Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density.</p><p>In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis.</p><p>Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems.</p><p>However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images.</p><p>Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.</p>

Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density. In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis. Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems. However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images. Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.