Nowadays, high portion of tactical missiles use gimbaled seeker. For accurate target tracking, the platform where the gimbal is mounted must be stabilized with respect to the motion of the missile body. Line of sight stabilization is critical for fast and precise tracking and alignment. Although, conventional PID framework solves many stabilization problems, it is reported that many PID feedback loops are poorly tuned. In this thesis, recently introduced robot control method, proxy based sliding mode control, is adopted for the line of sight (LOS) stabilization. Before selecting the proposed method, adaptive neural network sliding mode control and fuzzy control are also implemented for comparative purposes. Experimental and simulation results show a satisfactory response of the proxy based sliding mode controller.

This thesis addresses three problems related to image registration, prediction and tracking, applied to Angiography and Oncology. For image analysis, various probabilistic models have been employed to characterize the image deformations, target motions and state estimations. (i) In Digital Subtraction Angiography (DSA), having a high quality visualization of the blood motion in the vessels is essential both in diagnostic and interventional applications. In order to reduce the inherent movement artifacts in DSA, non-rigid image registration is used before subtracting the mask from the contrast image. DSA image registration is a challenging problem, as it requires non-rigid matching across spatially non-uniform control points, at high speed. We model the problem of sub-pixel matching, as a labeling problem on a non-uniform Markov Random Field (MRF). We use quad-trees in a novel way to generate the non uniform grid structure and optimize the registration cost using graph-cuts technique. The MRF formulation produces a smooth displacement field which results in better artifact reduction than with the conventional approach of independently registering the control points. The above approach is further improved using two models. First, we introduce the concept of pivotal and non-pivotal control points. `Pivotal control points' are nodes in the Markov network that are close to the edges in the mask image, while 'non-pivotal control points' are identified in soft tissue regions. This model leads to a novel MRF framework and energy formulation. Next, we propose a Gaussian MRF model and solve the energy minimization problem for sub-pixel DSA registration using Random Walker (RW). An incremental registration approach is developed using quad-tree based MRF structure and RW, wherein the density of control points is hierarchically increased at each level M depending of the features to be used and the required accuracy. A novel numbering scheme of the control points allows us to reuse the computations done at level M in M + 1. Both the models result in an accelerated performance without compromising on the artifact reduction. We have also provided a CUDA based design of the algorithm, and shown performance acceleration on a GPU. We have tested the approach using 25 clinical data sets, and have presented the results of quantitative analysis and clinical assessment. (ii) In External Beam Radiation Therapy (EBRT), in order to monitor the intra fraction motion of thoracic and abdominal tumors, the lung diaphragm apex can be used as an internal marker. However, tracking the position of the apex from image based observations is a challenging problem, as it undergoes both position and shape variation. We propose a novel approach for tracking the ipsilateral hemidiaphragm apex (IHDA) position on CBCT projection images. We model the diaphragm state as a spatiotemporal MRF, and obtain the trace of the apex by solving an energy minimization problem through graph-cuts. We have tested the approach using 15 clinical data sets and found that this approach outperforms the conventional full search method in terms of accuracy. We have provided a GPU based heterogeneous implementation of the algorithm using CUDA to increase the viability of the approach for clinical use. (iii) In an adaptive radiotherapy system, irrespective of the methods used for target observations there is an inherent latency in the beam control as they involve mechanical movement and processing delays. Hence predicting the target position during `beam on target' is essential to increase the control precision. We propose a novel prediction model (called o set sine model) for the breathing pattern. We use IHDA positions (from CBCT images) as measurements and an Unscented Kalman Filter (UKF) for state estimation. The results based on 15 clinical datasets show that, o set sine model outperforms the state of the art LCM model in terms of prediction accuracy.

Actuellement, les machines Asynchrones à Double Alimentation (MADA) sont omniprésentes dans le secteur éolien, grâce à leur simplicité de construction, leur faible coût d’achat et leur robustesse mécanique ainsi que le nombre faible d’interventions pour la maintenance. Cependant, comme toute autre machine électrique, ces génératrices sont sujettes aux défauts de différent ordre (électrique, mécanique, électromagnétique…) ou de différents types (capteur, actionneur ou composants du système). C’est pourquoi, il est primordial de concevoir une approche de diagnostic permettant de manière anticipée, de détecter, localiser et identifier tout défaut ou anomalie pouvant altérer le fonctionnement sain de ce type de machine. Motivés par les points forts des méthodes de diagnostic de défauts à base d’observateurs, nous proposons d’une part, dans cette thèse, une approche de détection, localisation et identification des défauts de la MADA d’une éolienne à vitesse variable, à base des observateurs de Kalman, performants et largement utilisés. Les erreurs d’estimation d’état du filtre de Kalman linéaire et de ses variantes non-linéaires, à noter : le Filtre de Kalman Etendu (EKF) et le Filtre de Kalman sans-Parfum (UKF), sont utilisés comme résidus sensibles aux défauts. En vue d’éviter les fausses alarmes et de découpler les défauts des perturbations et des bruits, l’analyse des résidus générés est réalisée par des tests statistiques tels que : Test de Page Hinkley (PH) et Test DCS (Dynamic Cumulative Sum). Pour la localisation des défauts multiples et simultanés, la Structure d’Observateurs Dédiés (DOS) et la Structure d’Observateurs Généralisés (GOS) sont appliquées. De plus, l’amplitude du défaut est déterminée dans l’étape d’identification de défaut. Les défauts capteurs, actionneurs et composants de la MADA, sont traités dans ce travail de recherche. D’autre part, une étude comparative entre les différents observateurs de Kalman, est élaborée. La comparaison porte sur les critères suivants : le temps de calcul, la précision et la vitesse de convergence des estimations. / Actually, the Doubly Fed Induction Generators (DFIG) are omnipresent in the wind power market, owing to their construction simplicity, their low purchase cost and their mechanical robustness. However, as any other electrical machine, these generators are subject to defects of different order (electrical, mechanical, electromagnetic ...) or of different type (sensor, actuator or system). That’s why, it is important to design an effective diagnostic approach, able to early detect, locate and identify any defect or abnormal behavior, which could undermine the healthy operation of this machine On the one hand, motivated by the observer-based fault diagnosis methods strengths, we proposed, in this thesis, a diagnostic approach for the faults detection, localization and identification of the DFIG used in variable speed wind turbine. This approach is based on the use of the efficient and widely used Kalman observers. The state estimation errors of the linear Kalman filter and the non-linear Kalman filters, named: The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are used as faults sensitive residuals. In order to avoid false alarms and to decouple faults from disturbances and noises, the faults detection is carried out by the analysis of the residuals generated, by the mean of statistical tests such as: Hinkley Page Test (PH) and DCS Test (Dynamic) Cumulative Sum). For the localization step in case of multiple and simultaneous faults, the Dedicated Observer scheme (DOS) and the Generalized Observer scheme (GOS) are applied. In addition, the fault level is determined in the fault identification step. Sensor faults, actuator and system faults of DFIG, are treated in this research work. On the other hand, a comparative study between the three Kalman observers proposed is performed. The comparison was done in terms of (1) the computation time, (2) the estimation accuracy, and (3) the convergence speed.