The goal of this diploma work is to design a control for permanent magnet synchronous motor in sliding mode. The introduction describes a synchronous motor, sensors which are used in the control process as well as power devices. Further, there is description of mathematical model for the synchronous motor. Another part of the diploma work deals with classic vector control for this kind of motor followed by the sliding mode control for the motor. At the end, there is presented an algorithm which has been implemented in the processor designed for this equipment. All the controllers used in the equipment have been tested on motor simulations. In conclusion, there is a target analysis and facts finding.

This thesis tries to research the term “robust controllers”. Its aim is to compare the robustness of discrete PID controllers (Discrete Equivalent Continuous Controller, Discrete Impulse Area Invariant, Takahashi, Feed-Forward), adaptive discrete PID controllers (Discrete Impulse Area Invariant, Takahashi, Feed-Forward), optimal controllers (quadratic optimal), and adaptive optimal controllers (quadratic optimal) on chosen transfer functions. Its aim is also to check the influence of A/D and D/A converters. The aims to obtain are demarked at the beginning of the text and also there is an explanation of the term “robustness.” Later on there is a description and an approximation to each of the chosen kinds of controllers and the identification methods used in the thesis (for adaptive controllers the method of recursive least-squares was used). The Kharitonov's Theorem are made on the chosen transfer function. Next there is a description of the methods with which the robustness of the controllers will be tested. The first method is the integral criteria, particular ITAE criterion and quadratic criterion. The second one is the analysis of the generalised circle criterion. Furthermore there are various displays of the results obtained and their corresponding comments. The results obtained are graphically displayed and by means of these schemes the particular types of controllers are compared. All of the simulations and results obtained were acquired through the use of the program MATLAB- Simulink. In the end of the thesis there is an overall evaluation.

This thesis deals with the modern control approaches applicable to speed control of an induction motor. Historical perspective to the control theory and its evolution to the modern control will be presented in a short introduction. Basics of uncertainty modeling are presented along with linear fractional transformation (LFT) representation of an uncertain system. Two different approaches for robust controller synthesis are introduced - H-infinity loopshaping and mixed sensitivity H-infinity synthesis. Theoretical background is presented for both of these methods. Finally the robust controller for induction motor satisfying the control goals is designed using both methods. Design objectives are presented as transfer function weights shaping the sensitivity or complementary sensitivity function to desired shape. Several step responses were simulated to compare H-infinity loopshaping and mixed sensitivity H-infinity controllers with the conventional vector control approach.

L’utilisation des AUV pour une exploitation durable des ressources océaniques est pertinente. Un robot sous-marin peut être utilisé comme plateforme pour observer, recueillir des informations sur l’environnement marin. Afin d’améliorer la qualité des observations et d’augmenter la capacité de navigation, de nombreuses questions doivent être abordées et examinées simultanément. Nous abordons ici le problème du pilotage de ces robots autonomes.Atteindre la maniabilité nécessaire dépend de deux facteurs clés: un modèle hydrodynamique précis et un système de contrôle performant. Cependant, le coût de développement d’un modèle précis est généralement très élevé. De plus, lorsque la géométrie du robot est complexe, il devient très difficile d’identifier de manière pertinente les paramètres dynamiques et hydrodynamiques. En outre, du point de vue de la commande, les modèles obtenus sont non linéaire, en particuliers pour les amortissements.De nombreux phénomènes dynamiques ne sont pas modélisés: dynamiques internes au robot, environnementales, liées aux bruits des capteurs, aux retards intrinsèques. Dans les concours de robotique sous-marine, il est confirmé que le traditionnel régulateur Proportionnel-Intégral-Dérivé (PID) est peu efficace pour les robots légers. Dans ce cas, notre champ d’application est plus axé sur la combinaison des approches de modélisation numérique et la commande robuste.Dans ce travail, nous proposons un schéma de régulation basé sur la commande robuste et la modélisation. La régulation robuste a été mise en place et validée en mer sur un AUV de la marque CISCREA et la solution proposée utilise Computational Fluid Dynamic (CFD) pour caractériser les deux paramètres hydrodynamiques (matrice de masse ajoutée et matrice d’amortissement). Puis un modèle à quatre degrés de liberté est construit pour le CISCREA. Les résultats numériques et expérimentaux sont alors comparés.La commande robuste proposée est basée sur une compensation non linéaire et de la commande H∞. La validation de la robustesse a été testée par simulation en Matlab et finalement validée par des essais en mer à Brest. La simulation et l’expérience montrent que l’approche en plus d’être robuste est plus rapide que les régulateurs précédemment proposés. / Autonomous Underwater Vehicle (AUV) is a relevant technology for the sustainable use of ocean resources. AUV can be used as an important ocean observing platform to collect information on marine environmental characteristics for research and industry fields. In order to improve the observation quality and increase the navigation ability, many issues should be addressed and considered simultaneously. Achieve necessary maneuverability depends on two key factors: an accurate hydrodynamic model and an advanced control system. However, the cost to develop an accurate hydrodynamic model, which shrinks the uncertainty intervals, is usually high. Meanwhile, when the robot geometry is complex, it becomes very difficult to identify its dynamic and hydrodynamic parameters. In addition, according to the quadratic damping factor, underwater vehicle dynamic and hydrodynamic model is nonlinear from the control point of view. Moreover, unmodeled dynamics, parameter variations and environmental disturbances create significant uncertainties among the nominal model and the reality. Sensor noise, signal delay as well as unmeasured states also affect the stability and control performance of the motion control system. In many of our underwater competitions, it has been confirmed that the traditional Proportional-Integral-Derivative (PID) regulation is less efficient for low mass AUV. In this case, our scope is more focused on the combination of numerical modeling approaches and robust control schemes. In this work, we proposed a model based robust motion control scheme. Without loss of generality, a robust heading controller was implemented and validated in the sea on cubic-shaped CISCREA AUV. The proposed solution uses cost efficient Computational Fluid Dynamic (CFD) software to predict the two hydrodynamic key parameters: The added mass matrix and the damping matrix. Four Degree of Freedom (DOF) model is built for CISCREA from CFD calculation. Numerical and experimental results are compared. Besides, the proposed control solution inherited the numerically obtained model from previous CFD calculation. Numerically predicted the actuator force compensates the nonlinear damping behavior result in a linear model with uncertainties. Based on the bounded linear nominal model, we proposed H∞ approach to handle the uncertainties, we used kalman filter to estimate unmeasured states such as angular velocity and we developed smith compensator to compensate the sensor signal delay. The proposed robust heading control application uses only one compass as feedback sensor. This is important while AUV is working at certain depth where only magnetic sensors still work. Our robust control scheme was simulated in Matlab and validated in the sea near Brest. Simulation shows obvious advantage of the proposed robust control approach. Meanwhile, the proposed robust heading control is much faster than PID controller. The robust controller is insensitive to uncertainties and has no overshot. From both simulations and real sea experiments, we found our proposed robust control approach and the one compass heading control applications are efficient for low mass and complex-shaped AUV CISCREA.

Récemment, le robot dirigeable a attiré l'attention de plus en plus des chercheurs grâce à ses avantages par rapport à d'autres aéronefs, tels que la capacité de VTOL, le vol stationnaire et à basse vitesse, une grande autonomie, et une interaction Homme-Robot sûre, etc. Ainsi c'est une plate-forme idéale pour diverses applications d'intérieur. Dans cette thèse, nous étudions la modélisation et le contrôle du mouvement d'un robot dirigeable d'intérieur et développons un prototype pour les opérations intérieures comme la surveillance. Le travail est composé de parties théoriques et pratiques. Concernant la partie théorique, d’abord, sous des hypothèses raisonnables, le modèle dynamique à 6-DOF est simplifié et divisé en deux parties indépendantes: le mouvement de l’altitude et le mouvement dans le plan horizontal. Ensuite, à fin d'assurer la précision de la modélisation et du contrôle, le modèle nominal est complété par des termes de perturbation qui sont estimés en temps réel et compensés dans les contrôleurs conçus. Des simulations sont effectuées pour vérifier les performances et la robustesse des contrôleurs. Pour la partie pratique du travail, basée sur l'analyse des fonctionnalités du robot afin de réaliser les applications intérieures souhaitées, le matériel du robot dirigeable est conçu et créé. Enfin, de vrais tests sont effectués sur la plate-forme de robot dirigeable pour la validation des lois de contrôle de mouvement conçues, et des résultats satisfaisants sont obtenus. / Recently, the blimp robot has attracted more and more attentions of the researchers for its advantages compared to other aircrafts, such as ability for VTOL, stationary and low speed flight, long endurance in air and safe Human-Robot interaction, etc. Therefore it is an ideal platform for various indoor applications. In this thesis, we study the modeling and motion control of an indoor blimp robot, and develop a real robot for indoor operations such as the long-term surveillance. The work is composed of both theoretical and practical parts. For the theoretical part, first, under reasonable assumptions, the 6-DOF dynamic model is simplified and divided into two independent parts: the altitude motion and the horizontal plane movement. Then, to ensure the accuracy of modeling and control, the nominal model is complemented with disturbance terms which are estimated in real-time and compensated in the designed controllers. Simulations are carried out to verify the performance and robustness of the controllers. For the practical part of the work, based on the functionality analysis of the robot to achieve desired indoor applications, the hardware of the blimp robot is conceived and created. Finally, real tests are made on the blimp robot platform for the validation of the designed motion control laws, and satisfying results are obtained.

Robust parameter estimation and pivotal inference is crucial for credible statistical conclusions. This thesis addresses these issues in three contexts: long-memory parameter estimation robust to low frequency nonstationary contamination, long-memory properties of financial time series, and inference on structural changes in a joint segmented trend with heterogeneous noise. Chapter 1 considers robust estimation of the long-memory parameter allowing for a wide collection of contamination processes, in particular low frequency nonstationary processes such as random level shifts. We propose a robust modified local-Whittle estimator and show it has the usual asymptotic distribution. We also provide modifications to further account for short-memory dynamics and additive noise. The proposed estimator provides substantial efficiency gains compared to existing methods in the presence of contaminations, without sacrificing efficiency when these are absent. Chapter 2 applies the modified local-Whittle estimator to various volatilities series for stock indices and exchange rates to robustly estimate the long-memory parameter. Our findings suggest that all series are a combination of long and short-memory processes and random level shifts, with the magnitude of each component varying across series. Our results contrast with the view that long-memory is the dominant feature. Chapter 3 is concerned with pivotal inference about structural changes in a joint segmented trend with heterogeneous noise. We provide tests for changes in the slope and the variance of the noise valid when both may be present, each allowed to occur at different dates. We suggest procedures for four testing problems.

This thesis is focused on the L1 regression, a possible alternative to the ordinary least squares regression. L1 regression replaces the least squares estimation with the least absolute deviations estimation, thus generalizing the sample median in the linear regres- sion model. Unlike the ordinary least squares regression, L1 regression enables loosening of certain assumptions and leads to more robust estimates. Fundamental theoretical re- sults, including the asymptotic distribution of regression coefficient estimates, hypothesis testing, confidence intervals and confidence regions, are derived. This method is then compared to the ordinary least squares regression in a simulation study, with a focus on heavy-tailed distributions and the possible presence of outlying observations. 1

The work in this dissertation concerns the construction of a robust iterative learning control (ILC) algorithm for a class of systems characterized by measurement delays, parametric uncertainty, and linear parameter varying (LPV) dynamics. One example of such a system is the twin roll strip casting process, which provides a practical motivation for this research. I propose three ILC algorithms in this dissertation that advance the state of the art. The first algorithm compensates for measurement delays that are longer than a single iteration of a periodic process. I divide the delay into an iterative and residual component and show how each component effects the asymptotic stability properties of the ILC algorithm. The second algorithm is a coupled delay estimation and ILC algorithm that compensates for time-varying measurement delays. I use an adaptive delay estimation algorithm to force the delay estimate to converge to the true delay and provide stability conditions for the coupled delay estimation and ILC algorithm. The final algorithm is a norm optimal ILC algorithm that compensates for LPV dynamics as well as parametric uncertainty and time delay estimation error. I provide a tuning method for the cost function weight matrices based on a sufficient condition for robust convergence and an upper bound on the norm of the error signal. The functionality of all three algorithms is demonstrated through simulated case studies based on an identified system model of the the twin roll strip casting process. The simulation testing is also augmented with experimental testing of select algorithms through collaboration with an industrial sponsor.

Abstract This thesis project is divided in two parts. The first part examines the possibility that correlation matrix estimates based on an outlier sample would contain information about extreme events. According to my findings, such methods do not perform better than simple shrinkage methods where robust shrinkage targets are used. The method tested is especially outperformed when it comes to the extreme events, where a shrinkage of the correlation matrix towards the identity matrix seems to give the best result. The second part is about valuation of skewness in marginal distributions and the penalizing of heavy tails. I argue that it is reasonable to use a degrees of freedom parameter instead of kurtosis and a certain regression parameter, that I develop, instead of skewness due to robustness issues. When minimizing the one period draw-down is our target, the "value" of skewness seems to have a linear relationship with expected returns. Re-valuing of expected returns, in terms of skewness, in the standard Markowitz framework will tend to lower expected shortfall (ES), increase skewness and lower the realized portfolio variance. Penalizing of heavy tails will most times in the same way lower ES, lower kurtosis and realized portfolio variance. The results indicate that the parameters representing higher order moments in some way characterize the assets and also reflect their future behavior. These properties can be used in a simple optimization framework and seem to have a positive impact even on portfolio level

Ultra-black materials reflect less than 1% of incident light, and are used in a wide variety of applications from low signal detector systems, to jewelry. The darkest ultra-black materials are made with vertically aligned carbon nanotubes (VACNTs). One downside to these VACNT based ultra-black material, is they are extremely fragile, and the types of surfaces they can be grown on is limited. Here I created a strengthened ultra-black material that can withstand light handling and drying from water exposure, and can be transferred to other substrates while remaining ultra-black. I also present theoretical models with supporting data on how to make the current darkest films even darker. I was able to create a material that had a 0.008% reflectance, making it the new darkest material. Using VACNTs as a scaffold, I created high aspect-ratio patterned VACNT structures that were encompassed by a carbon encapsulation layer. I was able to expose weaknesses in the encapsulation layer by depositing a thin layer of silicon on the VACNTs inside the carbon encapsulation. Inserting lithium into the silicon caused the silicon to expand, resulting in stress on the encapsulation layer. This strain from the silicon stressed the encapsulation layer of the different geometries, exposing weak points. Using VACNTs as a template, I created high aspect ratio 3D structures made from copper using Galvanic displacement and electroplating.