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.

A tendência crescente na geração de harmônicos nos sistemas elétricos de potência tem ganhado atenção especial no planejamento das redes de transporte de energia elétrica, uma vez que os crescimentos observados acontecem em regiões que antes não representavam qualquer tipo de preocupação. Um dos principais fatores que contribuíram para esse novo contexto é a característica distribuída da geração de harmônicos. Devido a essa nova realidade, métodos mais aprimorados para avaliação de desempenho e modelos mais precisos para a representação de equipamentos tornaram-se necessários. Sendo assim, a pesquisa realizada para a elaboração da presente tese fundamentou a sua investigação em três tópicos com o intuído de fornecer contribuições que permitissem uma avaliação mais precisa das redes elétricas, proporcionando, assim, resultados mais aderentes com a realidade existente: Modelagem Agregada de Carga; Equivalentes de Redes; Estimação de Estados das Distorções Harmônicas. Através das contribuições feitas nesses tópicos, torna-se possível a consideração de aspectos que antes eram ignorados na avaliação harmônica das redes de transporte de energia elétrica e, assim, permite-se uma verificação precisa dos impactos da característica distribuída da geração de harmônicos nos sistemas elétricos de potência. / The growing rate of harmonic generation present in the electric power systems has gained special attention in the planning process of power networks. The major factor that contributed for this new context is the increasing harmonic generation observed in regions that did not use to represent any concern in the past. One of the main causes for this new trend is the distributed characteristic of the harmonic generation. In this new environment, sophisticated methods and models have become necessary, in order to precisely represent the electric elements behaviour and to accurately evaluate the systems performance. As a result, the research work presented in this thesis focused in three different topics, in order to provide contributions that would lead to a more accurate performance evaluation of the power networks and that would provide results closer to the values found in the field: Aggregate Load Modeling; Network Equivalents; Harmonic State Estimation. These contributions would allow the consideration of aspects that normally are ignored in the harmonic assessment of power systems. Consequently, the evaluation of the impacts caused by the distributed generation of harmonics becomes more accurate.

<p>U ovoj doktorskoj disertaciji predložena je nova metoda za detekciju malicioznih napada injektiranjem loših merenja na elektroenergetski sistem. Predloženi algoritam baziran je na sinergiji statičke i dinamičke estimacije stanja, i u stanju je da detektuje ovaj tip napada u realnom vremenu, za najkritičniji scenario gde napadač ima potpuno znanje o sistemu, i neograničen pristup resursima.</p> / <p>This PhD thesis proposes a novel method for detection of malicious false data<br />injection attacks on power system. The proposed algorithm is based on<br />synergy between static and dynamic state estimators, and is capable of<br />detecting the forementioned attacks in real time, for the most critical scenarios,<br />where an attacker has complete knowledge about the compromised power<br />system and unlimited resources to stage an attack.</p>

The ability to implement Wide Area Monitoring and Control in power systems is developing into a need in order to prevent wide scale cascading outages. Monitoring of events in the power system provides a great deal of insight into the behaviour of the system. The research work presented in this thesis focussed on two tools that aid in monitoring: State Estimation and Synchronised Phasors provided by Phasor Measurement Units (PMU). State Estimation is essentially an on-line data processing scheme used to estimate the best possible state (i.e. voltage phasors) from a monitored set of measurements (active and reactive powers/voltage phasor measurements). The ever growing complexity and developments in the state of art calls for robust state estimators that converge accurately and rapidly. Newton’s method forms the basis for most of the solution approaches. For real-time application in modern power systems, the existing Newton-based state estimation algorithms are too fragile numerically. It is known that Newton’s algorithm may fail to converge if the initial nominal point is far from the optimal point. Sometimes Newton’s algorithm can converge to a local minima. Also Newton’s step can fail to be a descent direction if the gain matrix is nearly singular or ill-conditioned. This thesis proposes a new and more robust method that is based on linear programming and trust region techniques. The proposed formulation is suitable for Upper Bound Linear Programming. The formulation is first introduced and its convergence characteristics with the use of Upper Bound Linear Programming is studied. In the subsequent part, the solution to the same formulation is obtained using trust region algorithms. Proposed algorithms have been tested and compared with well known methods. The trust region method-based state estimator is found to be more reliable. This enhanced reliability justifies the additional time and computational effort required for its execution. One of the key elements in the synchrophasor based wide area monitoring is the Phasor Measurement Unit. Synchronized, real time, voltage phasor angle, phasor measurements over a distributed power network presents an excellent opportunity for major improvements in power system control and protection. Two of the most significant applications include state estimation and instability prediction. In recent years, there has been a significant research activity on the problem of finding the suitable number of PMUs and their optimal locations. For State Estimation, such procedures, which basically ensure observability based on network topology, are sufficient. However for instability prediction, it is very essential that the PMUs are located such that important/vulnerable buses are also directly monitored. In this thesis a method for optimal placement of PMUs, considering the vulnerable buses is developed. This method serves two purposes viz., identifying optimal locations for PMU (planning stage), and identifying the set PMUs to be closely monitored for instability prediction. The major issue is to identify the key buses when the angular and voltage stability prediction is taken into account. Integer Linear Programming technique with equality and inequality constraints is used to find out the optimal placement set. Further, various aspects of including the Phasor Measurements in state estimation algorithms are addressed. Studies are carried out on various sample test systems, an IEEE 30-bus system and real life Indian southern grid equivalents of 24-bus system, 72-bus system and 205-bus system.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

This thesis presents a comprehensive example framework on how current multiple model state estimation algorithms for jump Markov linear systems can be improved. The possible improvements are categorized as: -Design of multiple model state estimation algorithms using new criteria. -Improvements obtained using existing multiple model state estimation algorithms. In the first category, risk-sensitive estimation is proposed for jump Markov linear systems. Two types of cost functions namely, the instantaneous and cumulative cost functions related with risk-sensitive estimation are examined and for each one, the corresponding multiple model estate estimation algorithm is derived. For the cumulative cost function, the derivation involves the reference probability method where one defines and uses a new probability measure under which the involved processes has independence properties. The performance of the proposed risk-sensitive filters are illustrated and compared with conventional algorithms using simulations. The thesis addresses the second category of improvements by proposing -Two new online transition probability estimation schemes for jump Markov linear systems. -A mixed multiple model state estimation scheme which combines desirable properties of two different multiple model state estimation methods. The two online transition probability estimators proposed use the recursive Kullback-Leibler (RKL) procedure and the maximum likelihood (ML) criteria to derive the corresponding identification schemes. When used in state estimation, these methods result in an average error decrease in the root mean square (RMS) state estimation errors, which is proved using simulation studies. The mixed multiple model estimation procedure which utilizes the analysis of the single Gaussian approximation of Gaussian mixtures in Bayesian filtering, combines IMM (Interacting Multiple Model) filter and GPB2 (2nd Order Generalized Pseudo Bayesian) filter efficiently. The resulting algorithm reaches the performance of GPB2 with less Kalman filters.

A power system is typically one small part of a larger interconnected network and is affected to a varying degree, by contingencies external to itself as well as by the reaction of external network to its own contingencies. Thus, the accuracy of a complete interconnected network model would affect the results of many transmission level analyses. In an interconnected power system, the real-time network security and power transfer capability analyses require a ¡§real-time¡¨ complete network base case solution. In order to accurately assess the system security and the inter-area transfer capability, it is highly desirable that any available information from all areas is used. With the advent of communications among operations control center computers, real-time telemetered data can be exchanged for complete network modeling. Measurement time skew should be considered in the complete network modeling when combining large area data received via a data communication network. In this dissertation, several suggestions aiming toward the improvement of complete network modeling are offered. A discrete event simulation technique is used to assess the performance of a data exchange scheme that uses Internet interface to the SCADA system. Performance modeling of data exchange on the Internet is established and a quantitative analysis of the data exchange delay is presented. With the prediction mechanisms, the effect of time skew of interchanged data among utilities can be minimized, and consequently, state estimation (SE) could provide the accurate real-time complete network models of the interconnected network for security and available transfer capability analyses. In order to accommodate the effects of randomly varying arrival of measurement data and setup a base case for more accurate analyses of network security and transfer capability, an implementation of a stochastic Extended Kalman Filter (EKF) algorithm is proposed to provide optimal estimates of interconnected network states for systems in which some or all measurements are delayed. To have an accurate state estimation of a complete network, it is essential to have the capability of detecting bad data in the model. An efficient information debugging methodology based on the stochastic EKF algorithm is used for the detection, diagnosis and elimination of bad data.

The thesis outlines the development and application of a few novel dynamic state estimation based methods for estimation of parameters of vibrating engineering structures. The study investigates strategies for data fusion from multiple tests of possibly different types and different sensor quantities through the introduction of a common pseudo-time parameter. These strategies have been developed within the framework of Kalman and particle filtering techniques. The proposed methods are applied to a suite of problems that includes laboratory and field studies with a primary focus on finite element model updating of bridge structures and vehicle structure interaction problems. The study also describes how finite element models residing in commercially available softwares can be made to communicate with database of measurements via a particle filtering algorithm developed on the Matlab platform. The thesis is divided into six chapters and an appendix. A review of literature on problems of structural system identification with emphasis on methods on dynamic state estimation techniques is presented in Chapter 1. The problem of system parameter idenfification when measurements originate from multiple tests and multiple sensors is considered in Chapter 2. and solution based on Neumann expansion of the structural static/dynamic stiffness matrix and Kalman filtering is proposed to tackle this problem. The question of decoupling the problem of parameter estimation from state estimation is also discussed. The avoidance of linearization of the stiffness matrix and solution of the parameter problems by using Monte Carlo filters is examined in Chapter 3. This also enables treatment of nonlinear structural mechanics problems. The proposed method is assessed using synthetic and laboratory measurement data. The problem of interfacing structural models residing in professional finite element analysis software with measured data via particle filtering algorithm developed on Matlab platform is considered in Chapter 4. Illustrative examples now cover laboratory studies on a beam structure and also filed studies on an existing multi-span masonry railway arch bridge. Identification of parameters of systems with strong nonlinearities, such, as a rectangular rubber sheet with a concentric hole, is also investigated. Studies on parameter identification in beam moving oscillator problem are reported in Chapter 5. The efficacy of particle filtering strategy in identifying parameters of this class of time varying system is demonstrated. A resume of contributions made and a few suggestions for further research are provided in Chapter 6. The appendix contains details of development of interfaces among finite element software(NISA), data base of measurements and particle filtering algorithm (developed on Matlab platform).

Bislang existieren keine unter Praxisbedingungen einsetzbaren Messmethoden für eine verzögerungsfreie Bestimmung der für die Prozessführung wichtigen Zustandsgrößen in Biogasanlagen. Die Arbeit „Anwendung des erweiterten KALMAN-Filters zur Zustandsbeobachtung in Biogasanlagen“ hat zum Ziel, den Stand der Technik in der Biogaserzeugung dahingehend weiter zu entwickeln, dass mittels einer softwarebasierten Systemlösung die für eine fundierte Einschätzung des Prozesszustands einer Biogasanlage benötigten Zustandsgrößen sowohl im Fermenter als auch im Zulauf bestimmt werden können. Das praktische Interesse besteht insbesondere darin, dass die Bestimmung unter Verwendung von standardmäßig an Biogasanlagen verfügbaren Messsystemen sowie unter den anlagentechnischen Randbedingungen erfolgen kann. Grundlage für eine Systemlösung zur Zustandsbeobachtung in Biogasanlagen ist ein mathematisches Modell, das die relevanten Teilprozesse der Methangärung abbildet und stellvertretend für das Realstoffsystem mit mathematischen Methoden der Prozesszustands- und Parameterschätzung untersucht werden kann. Die entsprechend des Stands der Technik verfügbaren Prozessmodelle zur Beschreibung anaerober biologischer Abbauprozesse ermöglichen die Berechnung der Prozesszustandsgrößen der anaeroben Flüssigphase, sofern die Zusammensetzung des zugeführten Substrats bekannt ist. Diese ist jedoch in technischen Anlagen zur Biogaserzeugung in der Regel unbekannt, da die eingesetzten Substrate den nachwachsenden Rohstoffen sowie den biologischen Rest- und Abfallstoffen zuzuordnen sind, die herkunftsbedingt eine wechselnde Zusammensetzung und Verfügbarkeit aufweisen. Praxistaugliche, verzögerungsfreie Messmethoden für die Substratcharakterisierung stehen derzeit ebenfalls nicht zur Verfügung, so dass diese Modelle bislang nur eingeschränkte praktische Anwendbarkeit aufweisen. Zielstellung der Arbeit ist die Entwicklung einer Systemlösung, die es auf der Grundlage der mathematischen Beschreibung des Prozesses ermöglicht, die Prozesseingangsgrößen (Substratmenge und Substratzusammensetzung) und die Prozesszustandsgrößen (anaerobe Milieubedingungen) aus den Prozessmessgrößen (Gasmenge und Gaszusammensetzung) zu berechnen. Dabei sind unter praktischen Bedingungen auftretende Informationsverluste in den Messdaten infolge Biogasentschwefelung, Gaszwischenspeicherung und Leistungssteuerung des BHKW zu berücksichtigen, die zu keiner Beeinträchtigung der Anwendbarkeit führen dürfen. Mit Messwerten, die im Rahmen von zwei Versuchsreihen am Realstoffsystem bestimmt worden sind, wurde der Prozess der Methangärung für zwei spezielle Anwendungsfälle simuliert und einer Bewertung in Bezug auf die Qualität der Zustandsbeobachtung unterzogen. Die berechneten Verläufe ergaben eine hinreichend genaue Übereinstimmung mit den Verläufen der analytisch bestimmten Prozesszustands- und Prozesseingangsgrößen. Darauf aufbauend können dann Systeme zur Bewertung des Prozesszustands und zur Prozessregelung eingesetzt und zur Optimierung der Prozessführung in Biogasanlagen angewendet werden. / Suitable control of anaerobic digestion for biogas production requires well-founded knowledge about the process states. Standard measurement categories in small and medium scaled biogas plants contain gas analysis (amount and composition) and pH measurement. The actual process and input states of the liquid phase are usually unknown. This work presents a methodology for estimation of the unknown states based on standard measurement equipment. The system solution consists of two parts, a model describing the dynamics of the process and a two-stage identification of process states and model parameters. Within the first stage quadratic differences between simulated and measured values are minimized by the least square method; second stage minimizes the covariance matrix of the estimation error using the extended Kalman filter. Application of the system solution provides an high potential to increase efficiency of biogas production process and utilization of renewable resources.