Contrairement aux robots manipulateurs industriels qui sont de taille énorme et de prix élevé, beaucoup de robots manipulateurs à bas prix sont déjà entrés dans le marché, avec une petite taille, un poids léger, ce type de robots est plus accessible pour les particuliers. Cependant, limité par le coût de revient, des accessoires (matériaux, actuateurs, contrôleurs, etc) adoptés sont aussi limités, cela conduit souvent à la performance moins robuste au niveau de contrôle. Cette thèses se concentre sur la conception de contrôleur pour améliorer la performance des robots manipulateurs à bas prix. D'abord, pour des robots manipulateurs rigides, la modélisation dynamique en lien avec le système d'actualisation est établie, qui forme une équation différentielle avec paramètres constants et perturbation. Une méthode d'identification des paramètres en utilisant des observateurs et une commande adaptative sont proposées, et des résultats de simulation et d'expérimentation sont donnés. Ensuite, pour le cas d'articulation flexibles, pour simplifier, le modèle 1DOF est pris en compte. Premièrement, avec la mesure de la vitesse de lien, une méthode d'identification et une loi deux-étages adaptative sont proposées à condition que la position statique de lien puisse également être mesurée, des résultats de simulation sont donnés. Deuxièmement, en utilisant des mesures d'accélération de lien, une méthode d'identification et la même loi deux-étages adaptative sont proposées, cette idée est généralisée à l'identification et au contrôle de systèmes linéaires avec mesures de dérivées d'ordre élevé, des résultat de simulation sont présentés. Pour la mise en œuvre, des capteurs inertiels (gyroscopes et accéléromètres) sont utilisés et des résultats expérimentaux sont présentés. / Unlike industrial robot manipulators which are huge in size and of high price, many low-cost robot manipulators have already entered the market, with small size and light weight, this type of robots are more accessible to the public. However, limited by the cost, the components adopted (materials, actuators, controllers, etc.) are also limited, this often leads to less robust control performance. This thesis focuses on the controller design to improve the performance for such kind low-cost robot manipulators. To start with, for rigid case, dynamic modeling considering the actuator system is established, which forms a differential equation with constant parameters and disturbance, a method to identify the model parameters using observers and then an adaptive controller are proposed, simulation and experimental results are given. Then, in case of flexible joints, for simplicity, a single-link case model is considered. Firstly, link velocity measurement is assumed to provide link information, and an identification method and a two-stage adaptive control low are proposed provided that the static link position can also be measured, simulation result is given. Secondly, by using link acceleration measurement, an identification method and the same two-stage adaptive control low areproposed, this idea is generalized to identification and control of linear system using high-order derivative measurements, simulation result is presented. For implementation, inertial sensors (gyro and accelerometer) are used and experimental result is presented.

Les lymphocytes T sont une composante essentielle du système immunitaire de l'organisme. Ils peuvent reconnaître et répondre à un antigène étranger en vertu de leur récepteur d'antigène. En effet, les cellules T qui n'ont pas encore rencontrées des antigènes, sont appelées "naïves". Lors d'un premier contact antigénique, l'expansion clonale des lymphocytes T spécifiques a un antigène augmente fortement leur fréquence, et déséquilibre transitoirement de façon plus ou moins intense le compartiment lymphocytaire T périphérique. Cet équilibre doit être rétabli pour ne pas menacer à terme le bon fonctionnement du système immunitaire. Outre le risque de réponse explosive lors d'une réexposition à l'antigène, l'accumulation de clones T de taille disproportionnée gênerait considérablement le recrutement de lymphocytes T spécifiques de nouveaux antigènes. Ainsi, après élimination de l'antigène ou son confinement dans l'organisme, différents mécanismes interviennent. Il faut en effet d'une part assurer le maintien d'un compartiment de cellules T naïves de taille suffisante pour faire face à de nouvelles stimulations antigéniques. D'autre part, la constitution d'un panel de cellules T mémoires est nécessaire pour permettre une réponse immunitaire plus rapide et plus efficace lors de réexpositions antigéniques. Donc les mécanismes d'homéostasie des cellules T sont essentielles pour maintenir le nombre de cellules T à un niveau à peu près constant en contrôlant la division cellulaire et la mortalité des cellules. [...] / T lymphocytes are a fundamental component of the immune system that can recognise and respond to foreign antigens by virtue of their clonally expressed T cell antigen receptor (TCR). T cells that have yet to encounter the antigen they recognise are termed 'naive' as they have not been activated to respond. Homeostatic mechanisms maintain the number of T cells at an approximately constant level by controling cell division and death. In normal replete hosts, cell turnover within the naive compartment is very low and naive cells are maintained in a resting state.However, disruption of the homeostatic balance can arise from a wide variety of causes (viral infection (e.g. HIV), or drugs used in peritransplant induction therapy or cancer chemotherapy) and can result in T cell deciency or T lymphopenia. Under conditions of T lymphopenia, naive T cells undergo cell division with a subtle change in the cell surface phenotype (CD44 expression), termed homeostatic proliferation or lymphopenia induced proliferation (LIP). In this thesis, our purpose is to understand the process of T cell homeostatic through mathematical approach. At first, we build a new model that describes the proliferation of T cells in vitro under lymphopenic conditions. Our nonlinear model is composed of ordinary differential equations and partial differential equations structured by age (maturity of cell) and CD44 expression. To better understand the homeostasis of T cells, we identify the parameters that define T cell division by using experimental data. Next, we consider an age-structured model system describing the T cell homeostatic in vivo, and we investigate its asymptotic behaviour. Finally, an optimal strategy is applied in the in vivo model to rebuild immunity under conditions of T lympopenia.

Les travaux de cette thèse s'inscrivent dans le cadre du développement de méthodes d'identification inverse de paramètres matériau. On porte un intérêt particulier à la biomécanique des tissus souples renforcés par des fibres de collagène (artère, disque intervertébral, peau, tendon, ligament, etc.), dans le cadre de leurs réponses viscoélastiques et en grandes déformations et en grands déplacements (hyperélasticité). Fortement non-linéaires et anisotropes, les lois constitutives en biomécanique contiennent un nombre important de paramètres matériau. Le problème inverse qui permet de les identifier est de grande dimension et fortement non linéaire. En raison de difficultés numériques liées à sa résolution avec des méthodes à base de gradient, nous avons développé deux nouvelles méthodes d’identification inverse de paramètres nommées GAO (Genetic algorithms & Analytical Optimization) et MMIM (Maximum-Minimum Identification Method).La méthode GAO combine de manière avantageuse les méthodes déterministes de type gradient avec les algorithmes génétiques. Son originalité consiste à introduire des calculs analytiques pour la partie déterministe, ce qui permet d’accélérer et d’améliorer la convergence des algorithmes génétiques. Cette stratégie est appliquée dans le cadre de l’hyperélasticité anisotrope.En ce qui concerne la méthode MMIM, elle opère selon un critère d’identification basé sur la norme infinie et elle utilise les algorithmes génétiques. Elle permet d’identifier les paramètres de lois viscoélastiques quasi-linéaires. Elle garantit une réponse visqueuse constante qui est caractéristique des tissus souples qui sont insensibles à la vitesse de chargement.Les méthodes GAO et MMIM ont identifié avec succès des paramètres de tissus artériels et de tissus du disque intervertébral. Les propriétés de ces tissus sont décrits par ailleurs dans le mémoire dans un contexte plus général où on expose l'anatomie, l'histologie et le mécanisme de déformation aux différents niveaux hiérarchiques (nano-échelle à milli-échelle) d’un tissu souple renforcé par des fibres de collagène. Ceci permet de comprendre le rôle des efforts dans la relation liant la structure à la fonction en biologie. / This thesis focuses on research and development of inverse identification methods of material parameters. A particular attention is attributed to the viscoelastic response of collagen-reinforced soft tissues (artery, intervertebral disc, skin, tendon, ligament, etc) submitted to large displacements and large deformations (hyperelasticity). Highly non-linear and anisotropic, biomechanical constitutive laws account for a large number of material parameters. The inverse problem that allows their identification is of high non-linearity and of large dimension. By reason of numerical difficulties related to its resolution with gradient-based methods, we developed two new identification methods labelled GAO (Genetic algorithms & Analytical Optimization) and MMIM (Maximum-Minimum Identification Method).GAO advantageously combines deterministic methods of gradient type with genetic algorithms. Its originality consists in introducing analytical computations for the deterministic part leading to a gain in the speed up and in the convergence of genetic algorithms. This strategy is used in the context of anisotropic hyperelasticity.Regarding MMIM method, it operates according to an identification criterion that is expressed with the infinite norm and uses genetic algorithms. MMIM method identifies parameters of quasi-linear viscoelastic laws. It guarantees a constant viscous response that characterises the insensitivity of soft tissues to strain rate. GAO and MMIM methods successfully identified parameters of arterial wall and intervertebral disc tissues. The properties of these tissues are described in a more general context that exhibits the anatomy, histology and deformation mechanism at different hierarchical levels (nano-scale to milli-scale) of collagen-reinforced soft tissues. This gives understanding of the role of forces in relating structure to function in biology.

This master thesis studies the problem of parameter identification for system biology. Two methods have been studied. The method of interval analysis uses subpaving as a class of objects to manipulate and store inner and outer approximations of compact sets. This method works well with the model given as a system of differential equations, but has its limitations, since the analytical expression for the solution to the ODE is not always obtainable, which is needed for constructing the inclusion function. The other method, studied, is SDP-relaxation of a nonlinear and non-convex feasibility problem. This method, implemented in the toolbox bio.SDP, works with system of difference equations, obtained using the Euler discretization method. The discretization method is not exact, raising the need of bounding this discretization error. Several methods for bounding this error has been studied. The method of ∞-norm optimization, also called worst-case-∞-norm is applied on the one-step error estimation method. The methods have been illustrated solving two system biological problems and the resulting SCP have been compared. / Det här examensarbetet studerar problemet med parameteridentifiering för systembiologi. Två metoder har studerats. Metoden med intervallanalys använder union av intervallvektorer som klass av objekt för att manipulera och bilda inre och yttre approximationer av kompakta mängder. Denna metod fungerar väl för modeller givna som ett system av differentialekvationer, men har sina begränsningar, eftersom det analytiska uttrycket för lösningen till differentialekvationen som är nödvändigt att känna till för att kunna formulera inkluderande funktioner, inte alltid är tillgängliga. Den andra studerade metoden, använder SDP-relaxering, som ett sätt att komma runt problemet med olinjäritet och icke-konvexitet i systemet. Denna metod, implementerad i toolboxen bio.SDP, utgår från system av differensekvationer, framtagna via Eulers diskretiserings metod. Diskretiseringsmetoden innehåller fel och osäkerhet, vilket gör det nödvändigt att estimera en gräns för felets storlek. Några felestimeringsmetoder har studerats. Metoden med ∞-norm optimering, också kallat worst-case-∞-norm är tillämpat på ett-stegs felestimerings metoder. Metoderna har illustrerats genom att lösa två system biologiska problem och de accepterade parametermängderna, benämnt SCP, har jämförts och diskuterats.

Recently, permanent magnet assisted (PMa)-synchronous reluctance motors (SynRM) have been considered as a possible alternative motor drive for high performance applications. In order to have an efficient motor drive, performing of three steps in design of the overall drive is not avoidable. These steps are design optimization of the motor, identification of the motor parameter and implementation of an advanced control system to ensure optimum operation. Therefore, this dissertation first deals with the design optimization of the Permanent Magnet Assisted Synchronous Reluctance Motor (PMa-SynRM). Various key points in the rotor design of a low cost PMa-SynRM are introduced and their effects are studied. Finite element approach has been utilized to show the effects of these parameters on the developed average electromagnetic torque and the total d-q inductances. As it can be inferred from the name of the motor, there are some permanent magnets mounted in the rotor core. One of the features considered in the design of this motor is the magnetization of the permanent magnets mounted in the rotor core using the stator windings to reduce the manufacturing cost. At the next step, identification of the motor parameters is discussed. Variation of motor parameters due to temperature and airgap flux has been reported in the literatures. Use of off-line models for estimating the motor parameters is known as a computationally intensive method, especially when the models include the effect of cross saturation. Therefore in practical applications, on-line parameter estimation is favored to achieve a high performance control system. In this dissertation, a simple practical method for parameter estimation of the PMa-SynRM is introduced. Last part of the dissertation presents one advanced control strategy which utilized the introduced parameter estimator. A practical Maximum Torque Per Ampere (MTPA) control scheme along with a simple parameter estimator for PMa-SynRM is introduced. This method is capable of maintaining the MTPA condition and stays robust against the variations of motor parameters. Effectiveness of the motor design procedure and the control strategy is validated by presenting simulation and experimental results of a 1.5 kW prototype PMa-SynRM, designed and manufactured through the introduced design method.

Model Reference Adaptive Control (MRAC) is a widely studied adaptive control methodology that aims to ensure that a nonlinear plant with significant modeling uncertainty behaves like a chosen reference model. MRAC methods attempt to achieve this by representing the modeling uncertainty as a weighted combination of known nonlinear functions, and using a weight update law that ensures weights take on values such that the effect of the uncertainty is mitigated. If the adaptive weights do arrive at an ideal value that best represent the uncertainty, significant performance and robustness gains can be realized. However, most MRAC adaptive laws use only instantaneous data for adaptation and can only guarantee that the weights arrive at these ideal values if and only if the plant states are Persistently Exciting (PE). The condition on PE reference input is restrictive and often infeasible to implement or monitor online. Consequently, parameter convergence cannot be guaranteed in practice for many adaptive control applications. Hence it is often observed that traditional adaptive controllers do not exhibit long-term-learning and global uncertainty parametrization. That is, they exhibit little performance gain even when the system tracks a repeated command. This thesis presents a novel approach to adaptive control that relies on using current and recorded data concurrently for adaptation. The thesis shows that for a concurrent learning adaptive controller, a verifiable condition on the linear independence of the recorded data is sufficient to guarantee that weights arrive at their ideal values even when the system states are not PE. The thesis also shows that the same condition can guarantee exponential tracking error and weight error convergence to zero, thereby allowing the adaptive controller to recover the desired transient response and robustness properties of the chosen reference models and to exhibit long-term-learning. This condition is found to be less restrictive and easier to verify online than the condition on persistently exciting exogenous input required by traditional adaptive laws that use only instantaneous data for adaptation. The concept is explored for several adaptive control architectures, including neuro-adaptive flight control, where a neural network is used as the adaptive element. The performance gains are justified theoretically using Lyapunov based arguments, and demonstrated experimentally through flight-testing on Unmanned Aerial Systems.

No description available.

Using the dual Black-Scholes partial differential equation, Dupire derived an explicit formula, involving the ratio of partial derivatives of the evolving fair value of a European call option (ECO), for recovering information about its variable volatility. Because the prices, as a function of maturity and strike, are only available as discrete noisy observations, the evaluation of Dupire’s formula reduces to being an ill-posed numerical differentiation problem, complicated by the need to take the ratio of derivatives. In order to illustrate the nature of ill-posedness, a simple finite difference scheme is first used to approximate the partial derivatives. A new method is then proposed which reformulates the determination of the volatility, from the partial differential equation defining the fair value of the ECO, as a parameter identification activity. By using the weak formulation of this equation, the problem is localized to a subregion on which the volatility surface can be approximated by a constant or a constant multiplied by some known shape function which models the local shape of the volatility function. The essential regularization is achieved through the localization, the choice of the analytic weight function, and the application of integration-by-parts to the weak formulation to transfer the differentiation of the discrete data to the differentiation of the analytic weight function.

In this work, we propose an approach that allows to explore the potential pathophysiological mechanisms (at neuronal population level) of ictogenesis by combining clinical intracranial electroencephalographic (iEEG) recordings with a neural mass model. IEEG recordings from temporal lobe epilepsy (TLE) patients around seizure onset were investigated. Physiologically meaningful parameters (average synaptic gains of the excitatory, slow and fast inhibitory population, Ae, B and G) were identified during interictal to ictal transition. We analyzed the temporal evolution of four ratios, i.e. Ae/G, Ae/B, Ae/(B + G), and B/G. The excitation/inhibition ratio increased around seizure onset and decreased before seizure offset, suggesting the disturbance and restoration of balance between excitation and inhibition around seizure onset and before seizure offset, respectively. Moreover, the slow inhibition may have an earlier effect on the breakdown of excitation/inhibition balance. Results confirm the decrease in excitation/inhibition ratio upon seizure termination in human temporal lobe epilepsy, as revealed by optogenetic approaches both in vivo in animal models and in vitro. We further explored the distribution of the average synaptic gains in parameter space and their temporal evolution, i.e. the path through the model parameter space, in TLE patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during ictal and returned when the seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from individual patients. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing an identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy. The increase of excitation/inhibition ratios, i.e. Ae/G, Ae/B and Ae/(B+G), around seizure onset makes them potential cues for seizure detection. We explored the feasibility of a model based seizure detection algorithm. A simple thresholding method was employed. We evaluated the algorithm against the manual scoring of a human expert on iEEG samples from patients suffering from different types of epilepsy. Results suggest that Ae/(B+G), i.e. excitation/(slow + fast inhibition) ratio, allowed the best performance and that the algorithm best suited TLE patients. Leave-one-out cross-validation showed that the algorithm achieved 94.74% sensitivity for TLE patients. The median false positive rate was 0.16 per hour, and median detection delay was -1.0 s. Of interest, the values of the threshold determined by leave-one-out cross-validation for TLE patients were quite constant, suggesting a general excitation/inhibition balance baseline in background iEEG among TLE patients. Such a model-based seizure detection approach is of clinical interest and could also achieve good performance for other types of epilepsy provided that more appropriate model, i.e. better describe epileptic EEG waveforms for other types of epilepsy, is implemented. Altogether, this thesis contributes to the field of epilepsy research from two perspectives. Scientifically, it gives new insights into the mechanisms underlying interictal to ictal transition, and facilitates better understanding of epileptic seizures. Clinically, it provides a tool for reviewing EEG data in a more efficient and objective manner and offers an opportunity for on-demand therapeutic devices. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Le travail principal de cette thèse consiste à proposer une nouvelle procédure d'identification inverse appliquée aux situations de mise en forme et d'usinage des métaux, qui peut fournir un ensemble de paramètres appropriés pour toute loi constitutive elastoplastique suivant le modèle de plasticité de type J_{2} avec écrouissage isotrope, en évaluant la corrélation entre les réponses expérimentales et numériques. En premier lieu, un programme d'identification a été développé, en combinant l'algorithme de Levenberg-Marquardt et des méthodes de traitement de données pour identifier les paramètres constitutifs. En termes d'expérimentation, des essais de compression et de traction dynamiques ont été effectués. La forme finale déformée des spécimens, qui repose sur une analyse post-mortem, a été choisie comme quantité d'observation. Comme pour la simulation numérique, des modèles numériques de ces mêmes procédures expérimentales ont été construits en utilisant le code éléments finis Abaqus/Explicit afin de fournir des réponses numériques. Un algorithme numérique a été proposé pour l'implémentation de lois constitutives elastoplastiques définies par l'utilisateur dans Abaqus/Explicit. / The main concern of this thesis is to propose a new inverse identification procedure applied to metal forming and machining situations, which can provide an appropriate parameters set for any elastoplastic constitutive law following J_{2} plasticity and isotropic hardening, by evaluating the correlation between the experimental and numerical responses. Firstly the identification program has been developed, which combines the Levenberg-Marquardt algorithm and the Data processing methods to optimize the constitutive parameters. In terms of experimentation, dynamic compression and tensile tests have been conducted. The final deformed shape of specimens, which relies on a post-mortem analysis, has been selected as the observation quantity. As for the numerical simulation, the numerical models of the same experimental procedure have been built with the finite element software Abaqus/Explicit in order to provide numerical responses. A numerical algorithm has been proposed for the implementation of user defined elastoplastic constitutive laws in Abaqus/Explicit.