Model-based control methods to improve the power qualify of grid-connected single-phase inverters.

Power electronic converters are commonly used for interfacing distributing generation sources (DGs) to the electrical power system networks. This is necessary because these DGs usually have different output characteristics and cannot be connected directly to the local load and/or the grid. The power electronic front-end converter is an inverter whose dc link is fed by an ac/dc converter or by a dc/dc converter, according to the DG source type. The commercial front-end inverters are designed to operate either in grid-connected (GC) mode or in stand-alone (SA) mode. In the SA mode, the inverter is connected to local load, but in the GC mode the inverter must be connected to the utility grid and a local load could be connected to this system as well. Based on this, any designed or proposed controller for such systems should work well in both operation modes. The control objective in SA mode is to improve the quality of the local load voltage, and the control objective in GC mode is to inject clean current to the grid with low total harmonic distortion (THD). Most of the control schemes in the literature have been designed to work in one of these operation modes and ensure low THD either for the local load voltage or for the injected grid current. However, some of the existing control schemes in the literature proposed different control architectures for each operation mode. Moreover, there are a few researches have been reported in the literature based on the cascaded control theory to obtain low THD for both the local load voltage simultaneously with the injected current to the grid in the grid-connected mode. Due to the growing penetration of the DG sources in the residential applications, single-phase grid-connected inverters have gained much attention. For this reason, the single-phase grid-connected inverter systems have been chosen in our study. Since such systems have nonlinearity in its behavior, different nonlinear model-based control schemes have been designed in order to improve the quality of the local load voltage while injecting clean current to the grid for single-phase grid-connected inverter systems by using single structure control scheme. Furthermore, the proposed control schemes ensure the seamless transfer between GC and SA operation modes without adjusting the controller structure and with self-synchronization ability

Nonlinear observers for burning zone temperatures and torque estimation of the rotary cement kiln.

Due to consistent expansion in the infrastructure and housing sectors worldwide have given a new way for the rapid growth of global cement market. Increased global demand for the cement production makes the attractive research topic which can lead to the quality and overall efficiency of the product. Measurement of the temperature in the burning zone is vital to maintain product quality and kiln efficiency in the cement industry. Often the BZT is un-measurable due to internal kiln conditions, dusty environment, extreme heat, harshness for example and this leads to kiln not being driven as efficient as possible. Multi-physics tools are core to modern engineering, and smart manufacturing, but have not been extensively utilized in this low-cost industry, hence proposed approach is to find a reduced ordered model (ROM) of the thermodynamics of the kiln using data centric approach along with Multiphysics tool

Algorithmes quantiques pour la cryptanalyse et cryptographie symétrique post-quantique

Modern cryptography relies on the notion of computational security. The level of security given by a cryptosystem is expressed as an amount of computational resources required to break it. The goal of cryptanalysis is to find attacks, that is, algorithms with lower complexities than the conjectural bounds.With the advent of quantum computing devices, these levels of security have to be updated to take a whole new notion of algorithms into account. At the same time, cryptography is becoming widely used in small devices (smart cards, sensors), with new cost constraints.In this thesis, we study the security of secret-key cryptosystems against quantum adversaries.We first build new quantum algorithms for k-list (k-XOR or k-SUM) problems, by composing exhaustive search procedures. Next, we present dedicated cryptanalysis results, starting with a new quantum cryptanalysis tool, the offline Simon's algorithm. We describe new attacks against the lightweight algorithms Spook and Gimli and we perform the first quantum security analysis of the standard cipher AES.Finally, we specify Saturnin, a family of lightweight cryptosystems oriented towards post-quantum security. Thanks to a very similar structure, its security relies largely on the analysis of AES.La cryptographie moderne est fondée sur la notion de sécurité computationnelle. Les niveaux de sécurité attendus des cryptosystèmes sont exprimés en nombre d'opérations ; une attaque est un algorithme d'une complexité inférieure à la borne attendue. Mais ces niveaux de sécurité doivent aujourd'hui prendre en compte une nouvelle notion d'algorithme : le paradigme du calcul quantique. Dans le même temps,la délégation grandissante du chiffrement à des puces RFID, objets connectés ou matériels embarqués pose de nouvelles contraintes de coût.Dans cette thèse, nous étudions la sécurité des cryptosystèmes à clé secrète face à un adversaire quantique.Nous introduisons tout d'abord de nouveaux algorithmes quantiques pour les problèmes génériques de k-listes (k-XOR ou k-SUM), construits en composant des procédures de recherche exhaustive.Nous présentons ensuite des résultats de cryptanalyse dédiée, en commençant par un nouvel outil de cryptanalyse quantique, l'algorithme de Simon hors-ligne. Nous décrivons de nouvelles attaques contre les algorithmes Spook et Gimli et nous effectuons la première étude de sécurité quantique du chiffrement AES. Dans un troisième temps, nous spécifions Saturnin, une famille de cryptosystèmes à bas coût orientés vers la sécurité post-quantique. La structure de Saturnin est proche de celle de l'AES et sa sécurité en tire largement parti

Process Simulation of Technical Precipitation Processes - The Influence of Mixing

This work develops and shows up methods to tackle multi-scale challenges in particle formation during precipitation crystallization. Firstly, molecular, micro- and meso-scale interactions in confined impinging jet mixers are investigated and simulatively predicted. Secondly, to build up on developed methods, macroscale as present for instance in stirred tank reactors is added to the considerations

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Nonlinear control and observation of full-variable speed wind turbine systems.

With increasing concern for the environmental effects of power generation from fossil fuels, wind energy is a competitive source for electrical power with higher efficiency than other clean sources. However, the nature of this power source makes controlling wind turbines difficult. The variability of wind as a source either requires highly accurate measurement equipment or sophisticated mathematical alternatives. In addition to the unknown quantities of the weather itself, the efficiency of power capture at the turbine blades is highly nonlinear in nature and difficult to ascertain. The ability of either determine these troublesome quantities, or control the system despite ignorance of them, greatly increases the overall efficiency of power capture. To this end, a series of nonlinear controllers and observers have been developed for wind turbine systems

분산형 통신 및 구동부족 로봇시스템 을 위한 분할기법 기반의 반자율 원격제어 프레임워크 개발

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 이동준.The framework of stable bilateral teleoperation has been well established during decades. However, the standard bilateral teleoperation framework could be a baseline for a successful telerobotics but not sufficient for real-application because they usually concentrate on only the bilateral stability. The least considered in the previous research is how to apply a complex robot systems such as multiple mobile robots or a large degree of freedom mobile manipulators for real applications. The main challenges of teleoperation of complex robotic systems in real-world are to achieve two different control objectives (i.e., follow the human command and the coordination/ stabilization of the internal movement) of the slave robots simultaneously, while providing intuitive information about the complicated features of the system. In this thesis, we develop decomposition-based semi-autonomous teleoperation framework for robotic systems which have distributed communication and underactuation property, consisting of three steps: 1) decomposition step, where the human command is defined, and the robotic system is split into the command tracking space and its orthogonal complement (i.e., internal motion)2) control design of the slave robot, in which we design the slave controller for human command tracking and stabilization/coordination of internal motion spaceand 3) feedback interface design, through which we propose a multi-modal feedback interface (for example, visual and haptic) designed with the consideration of the task and the characteristics of the system. Among numerous types of robots, in this thesis, we focus on two types of robotic systems: 1) multiple nonholonomic wheeled mobile robots (WMRs) with distributed communication requirement and 2) manipulator-stage over vertical flexible beam which is under-actuated system. The proposed framework is applied to both case step by step and perform experiments and human subject study to verify/demonstrate the proposed framework for both cases. For distributed WMRs, we consider the scenario that a single user remotely operates a platoon of nonholonomic WMRs that distributively communicate each other in unknown environment. For this, in decomposition step, we utilize nonholonomic passive decomposition to split the platoon kinematics into that of the formation-keeping aspect and the collective tele-driving aspect. Next, in control design step, we design the controls for these two aspects individually and distribute them into each WMR while fully incorporating their nonholonomic constraint and distribution requirement. Finally, in the step of feedback interface design, we also propose a novel predictive display, which, by providing the user with the estimated current and predicted future pose informations of the platoon and future possibility of collision while fully incorporating the uncertainty inherent to the distribution, can significantly enhance the tele-driving performance and easiness of the platoon. The second part is the manipulator-stage over vertical flexible beam which is under-actuated system. Here, the human command defines the desired motion of the end-effector (or the manipulator), and the vibration of the beam should be subdued at the same time. Thus, at the first step, we utilize the passive decomposition to split the dynamics into manipulator motion space and its orthogonal complement, in which we design the control for the suppression of the vibration. For human command tracking, we design the passivity-based control, and, for the suppression of the vibration, we propose two controls: LQR-based control and nonlinear control based on Lyapunov function analysis. Finally, visuo-haptic feedback interface is preliminarily designed for successful peg-in-hole tasks.1 Introduction 1 1.1 Background and Contribution 1 1.2 Related Works 4 1.2.1 Related Works on Distributed Systems 5 1.2.2 Related Works on Manipulator-Stage System 6 1.3 Outline 6 2 Preliminary 7 2.1 Passive Decomposition 7 2.1.1 Basic Notations and Properties of Standard Passive Decomposition 7 2.1.2 Nonholonomic Passive Decomposition 9 3 Semi-Autonomous Teleoperation of Nonholonomic Wheeled Mobile Robots with Distributed Communication 11 3.1 Distributed Control Design 11 3.1.1 Nonholonomic Passive Decomposition 11 3.1.2 Control Design and Distribution 19 3.2 Distributed Pose Estimation 25 3.2.1 EKF Pose Estimation of Leader WMR 25 3.2.2 EKF Pose Estimation of Follower WMRs 28 3.3 Predictive Display for Distributed Robots Teleoperation 29 3.3.1 Estimation Propagation 31 3.3.2 Prediction Propagation 34 3.4 Experiments 38 3.4.1 Test Setup 38 3.4.2 Performance Experiment 39 3.4.3 Teleoperation Experiment with Predictive Display 40 3.4.4 Human Subject Study 44 4 Semi-Autonomous Teleoperatoin of Stage-Manipulator System on Flexible Vertical Beam 49 4.1 System Modeling 49 4.1.1 System Description 49 4.1.2 Assumed Mode Shapes 51 4.1.3 Exact Solution under Given Boundary Conditions 51 4.1.4 Euler-Lagrangian Equation 61 4.2 LQR-based Control Design 62 4.2.1 Passive Decomposition 63 4.2.2 Vibration Suppression Control Design 64 4.2.3 Joint Tracking Control Design 66 4.3 Lyapunov-based Control Design 68 4.3.1 Twice Passive Decomposition for Input Coupling 69 4.3.2 Interconnected System Description 70 4.3.3 Passivity-based Manipulator Motion Control 74 4.3.4 Dissipative Control for Vibration Suppression 74 4.4 Experiments 78 4.4.1 Test Setup 78 4.4.2 Joint Tracking and Vibration Suppression Experiment 81 4.4.3 Comparison Experiment between the LQR and the Nonlinear Control 82 5 Conclusion 83 5.1 Summary 83 5.2 Future Works 83 A Appendix 85 A.1 Internal Wrench Representation 85Docto

Contributions to improve the technologies supporting unmanned aircraft operations

Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

Optimization of Critical Infrastructure with Fluids

Many of the world's most critical infrastructure systems control the motion of fluids. Despite their importance, the design, operation, and restoration of these infrastructures are sometimes carried out suboptimally. One reason for this is the intractability of optimization problems involving fluids, which are often constrained by partial differential equations or nonconvex physics. To address these challenges, this dissertation focuses on developing new mathematical programming and algorithmic techniques for optimization problems involving difficult nonlinear constraints that model a fluid's behavior. These new contributions bring many important problems within the realm of tractability. The first focus of this dissertation is on surface water systems. Specifically, we introduce the Optimal Flood Mitigation Problem, which optimizes the positioning of structural measures to protect critical assets with respect to a predefined flood scenario. Two solution approaches are then developed. The first leverages mathematical programming but does not tractably scale to realistic scenarios. The second uses a physics-inspired metaheuristic, which is found to compute good quality solutions for realistic scenarios. The second focus is on potable water distribution systems. Two foundational problems are considered. The first is the optimal water network design problem, for which we derive a novel convex reformulation, then develop an algorithm found to be more effective than the current state of the art on select instances. The second is the optimal pump scheduling (or Optimal Water Flow) problem, for which we develop a mathematical programming relaxation and various algorithmic techniques to improve convergence. The final focus is on natural gas pipeline systems. Two novel problems are considered. The first is the Maximal Load Delivery (MLD) problem for gas pipelines, which aims at finding a feasible steady-state operating point that maximizes load delivery for a severely damaged gas network. The second is the joint gas-power MLD problem, which couples damaged gas and power networks at gas-fired generators. In both problems, convex relaxations of nonconvex dynamical constraints are developed to increase tractability.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169849/1/tasseff_1.pd