The simplified models of synchronous generators, widely used in stability studies of large electric power systems, are not completely suitable for the stability analysis and the design of controllers of distributed synchronous generators, generally connected to typically unbalanced branches. To more accurately analyze the systems with distributed generation, it is necessary to utilize synchronous generator models that consider frequency variation in their electrical equations. Furthermore, this model must represent possible unbalanced three-phase voltages at the generator terminals as well. Nonetheless, to provide reliable responses, the parameters of this more detailed model should be known. Thus, this work assesses the influence of the parameters on the responses of a detailed synchronous generator model, suitable to depict unbalanced operating conditions, and proposes an approach for the estimation of its most important parameters. In the proposed structure, we first employ Trajectory Sensitivity Functions to evaluate the dependency of the responses of this model with respect to its parameters and, from that, we rank them according to their importance. Subsequently, we apply an estimation process that utilizes the Unscented Kalman Filter with the aid of a genetic algorithm to estimate the main parameters of this synchronous generator model under unbalanced operating conditions. To obtain the results and, therefore, assess the proposed estimation approach, we make use of a system which comprises a synchronous generator connected to a three-phase unbalanced load. In addition to the unbalanced operation of the test system, we also consider noises due to the constant load switching, typical of distribution systems. The estimations performed for three operating conditions of the generator were very satisfactory, which demonstrates the efficiency of the proposed approach to obtain adequate models for the description of synchronous generator operation under unbalanced operating conditions. / Os modelos simplificados de geradores síncronos, amplamente utilizados em estudos de estabilidade de grandes sistemas elétricos de potência, não são completamente adequados para a análise de estabilidade e projetos de controladores dos geradores síncronos distribuídos, geralmente conectados a sistemas tipicamente desequilibrados. Para que os sistemas com geração distribuída possam ser analisados mais fidedignamente, é necessária a utilização de um modelo de gerador síncrono que considere a variação de frequência em suas equações elétricas. Além disso, esse modelo também deve ser capaz de representar possíveis tensões trifásicas desequilibradas nos terminais do gerador. Entretanto, para que esse modelo mais detalhado possa fornecer respostas coerentes com a realidade, deve-se conhecer seus parâmetros. Dessa forma, este trabalho avalia a influência dos parâmetros nas respostas de um modelo de gerador síncrono mais detalhado, adequado para representar operações desbalanceadas, e propõe uma abordagem para a estimação de seus parâmetros mais importantes. Nessa estrutura, inicialmente empregam-se as Funções de Sensibilidade de Trajetória para avaliar a dependência das respostas desse modelo em relação aos seus parâmetros e, a partir disso, ordená-los conforme sua importância. Em seguida, aplica-se um processo de estimação que utiliza o Filtro de Kalman Unscented com o auxílio de um algoritmo genético para estimar os principais parâmetros desse modelo de gerador síncrono em condições de desbalanço. Para a obtenção dos resultados e consequente avaliação da abordagem de estimação proposta, utiliza-se um sistema composto por um gerador síncrono conectado a uma carga trifásica desbalanceada. Além da operação desbalanceada desse sistema teste, também são considerados ruídos devidos ao constante chaveamento de cargas, típicos de sistemas de distribuição. As estimações realizadas para três condições de operação do gerador foram bem satisfatórias, indicando a eficiência da abordagem proposta na obtenção de modelos adequados para descrever a operação de geradores síncronos em condições de desbalanço.

O planejamento e o funcionamento do sistema elétrico de potência se baseiam na correta parametrização e caracterização de seus elementos, pois a correta parametrização dos sistemas de proteção permite uma atuação confiável e segura. Ademais, a correta caracterização dos parâmetros de elementos como as linhas de transmissão permite calcular o carregamento ótimo para determinados trechos do sistema interconectado com relação ao fluxo de potência, permitindo um melhor planejamento para expansão e instalação de reforços, dentre outros. Desta forma, foram desenvolvidas metodologias para estimação de parâmetros de sistemas de transmissão, que se baseiam na adoção de um modelo para o sistema e utilização de um método de resolução para se obter uma resposta, ou função de transferência, para as entradas e saídas deste modelo. No entanto, a maioria dos métodos existentes na literatura técnica apresenta certas limitações com relação à presença de ruído nos sinais de medição, ou dificuldade na relação existente entre as correntes longitudinais e as medições terminais, ou imprecisão do método de resolução do sistema de equações para determinada situação. Neste trabalho, foram utilizadas técnicas diferentes para a estimação de parâmetros de uma linha diretamente das medições das correntes e tensões terminais da linha em regime, sendo possível reduzir bastante o erro de estimação, pois não é necessário nenhum método de eliminação dos ruídos das medições. Desta forma, a corrente longitudinal é considerada como equivalente à última componente de corrente terminal, e a metodologia adotada apresenta erros pequenos de estimação independente do modelo adotado. Assim, modelam-se linhas de transmissão em cascata de circuitos ?, obtendo-se as medições ruidosas oriundas desta. Por fim, utiliza-se a metodologia baseada na teoria do Filtro de Kalman Unscented para eliminação do ruído nas grandezas medidas e para estimação dos parâmetros série da linha de transmissão. Através da ferramenta computacional Simulation and model-based design (Simulink), realizam-se a obtenção das medições, inclusão de ruído aleatório a estas e cálculos computacionais para estimação dos estados e parâmetros do sistema em regime permanente. / The planning and operation of a power system are based on the correct parameterization and characterization of its elements. By a correct parameterization of protection systems, a reliable and safe operation is allowed. The correct characterization of the parameters of transmission lines allows calculating the optimum power flow for the interconnected power system, allowing a better planning for expansion and installation of reinforcements, among others. In this sense, methodologies were developed for the estimation of transmission system parameters, which are based on the adoption of a model for the system and usage of a resolution method to obtain a response, or transfer function, for the inputs and outputs of the model. However, the most of existing methods in the technical literature presents certain limitations regarding presence of noise in the measurement signals, or difficulty regarding the relationship between the longitudinal currents and the terminal measurement signals, or imprecision of the solving method for the equations system. In this work, different techniques were applied for the estimation of line parameters directly from the measurements of the terminal currents and terminal voltages of the steady state line, being possible to greatly reduce the estimation error, since no extra method of eliminating measurements noise is necessary. In this way the longitudinal current is considered as equivalent to the last terminal current component, and the adopted methodology presents small estimation errors regardless of the adopted model. Thus, transmission lines are modeled by a pi cascade and noisy measurements are obtained. Finally, the methodology is based on the Unscented Kalman Filter theory for noise elimination in the measurements and for estimation of the transmission line longitudinal parameters. By the computational tool Simulink, measurements are obtained, random noise is inserted and computational calculations for estimation of the states and parameters of the system in steady state are done.

Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multi- carrier modulation scheme, which has been adopted for several wireless stan- dards. Systems employing this scheme at the physical layer are sensitive to frequency offsets and that causes Inter Carrier Interference (ICI) and degra- dation in overall system performance of OFDM systems. In this thesis work, an investigation on impairments of OFDM systems will be carried out. Anal- ysis of previous schemes for cancellation of the ICI will be done and a scheme for estimating and compensating the frequency offset based on Unscented Ka- man Filter (UKF) and Extended Kaman Filter (EKF) will be implemented. Analysis on how the UKF improves the Signal to Noise Ratio (SNR); and how well it tracks the frequency offset estimation under Additive White Gaussian Noise (AWGN) channel and flat fading Rayleigh channel will be carried on.

Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownship inertial position, inertial velocity, and attitude) that are directly used in the autopilot system, and the tracking system provides low-rate target tracking states (typically target relative position and velocity with respect to the ownship). Target states in the global frame can be obtained by adding the ownship states and the target tracking states. The data estimated from this combination of the navigation system and the tracking system provide key information for the design of most UAV guidance laws, control command generation, trajectory generation, and path planning. As a baseline system that estimates ownship states, an integrated navigation system is designed by using an extended Kalman filter (EKF) with sequential measurement updates. In order to effectively fuse various sources of aiding sensor information, the sequential measurement update algorithm is introduced in the design of the integrated navigation system with the objective of being implemented in low-cost autonomous UAV's. Since estimated state accuracy using a low-cost, MEMS-based IMU degrades with time, several absolute (low update rate but bounded error in time) sensors, including the GPS receiver, the magnetometer, and the altimeter, can compensate for time-degrading errors. In this work, the sequential measurement update algorithm in smaller vectors and matrices is capable of providing a convenient framework for fusing the many sources of information in the design of integrated navigation systems. In this framework, several aiding sensor measurements with different size and update rates are easily fused with basic high-rate IMU processing. In order to provide a new mechanism that estimates ownship states, a new nonlinear filtering framework, called the unscented Kalman filter (UKF) with sequential measurement updates, is developed and applied to the design of a new integrated navigation system. The UKF is known to be more accurate and convenient to use with a slightly higher computational cost. This filter provides at least second-order accuracy by approximating Gaussian distributions rather than arbitrary nonlinear functions. This is compared to the first-order accuracy of the well-known EKF based on linearization. In addition, the step of computing the often troublesome Jacobian matrices, always required in the design of an integrated navigation system using the EKF, is eliminated. Furthermore, by employing the concept of sequential measurement updates in the UKF, we can add the advantages of sequential measurement update strategy such as easy compensation of sensor latency, easy fusion of multi-sensors, and easy addition and subtraction of new sensors while maintaining those of the standard UKF such as accurate estimation and removal of Jacobian matrices. Simulation results show better performance of the UKF-based navigation system than the EKF-based system since the UKF-based system is more robust to initial accelerometer and rate gyro biases and more accurate in terms of reducing transient peaks and steady-state errors in ownship state estimation. In order to estimate target tracking states or target kinematics, a new vision-based tracking system is designed by using a UKF in the scenario of three-dimensional air-to-air tracking. The tracking system can estimate not only the target tracking states but also several target characteristics including target size and acceleration. By introducing the UKF, the new vision-based tracking system presents good estimation performance by overcoming the highly nonlinear characteristics of the problem with a relatively simplified formulation. Moreover, the computational step of messy Jacobian matrices involved in the target acceleration dynamics and angular measurements is removed. A new particle filtering framework, called an extended marginalized particle filter (EMPF), is developed and applied to the design of a new vision-based tracking system. In this work, only three position components with vision measurements are solved in particle filtering part by applying Rao-Blackwellization or marginalization approach, and the other dynamics, including the target nonlinear acceleration model, with Gaussian noise are effectively handled by using the UKF. Since vision information can be better represented by probabilistic measurements and the EMPF framework can be easily extended to handle this type of measurements, better performance in estimating target tracking states will be achieved by directly incorporating non-Gaussian, probabilistic vision information as the measurement inputs to the vision-based tracking system in the EMPF framework.

This thesis presents the design of Kalman filter based attitude determination algorithms for a hypothetical LEO satellite and for a satellite attitude control test setup. For the hypothetical LEO satellite, an Extended Kalman Filter based attitude determination algorithms are formed with a multi-mode structure that employs the different sensor combinations and as well as online switching between these combinations depending on the sensor availability. The performance of these different attitude determination modes are investigated through Monte Carlo simulations. New attitude determination algorithms are prepared for the satellite attitude control test setup by considering the constraints on the selection of the suitable sensors. Here, performances of the Extended Kalman Filter and Unscented Kalman Filter are investigated. It is shown that robust and sufficiently accurate attitude estimation for the test setup is achievable by using the Unscented Kalman Filter.

In this thesis study, a valveless hydraulic servo system controlled by two pumps is investigated and its performance characteristics are compared with a conventional valve controlled system both experimentally and analytically. The two control techniques are applied on the position control of a single rod linear actuator. In the valve controlled system, the flow rate through the actuator is regulated with a servovalve / whereas in the pump controlled system, two variable speed pumps driven by servomotors regulate the flow rate according to the needs of the system, thus eliminating the valve losses. To understand the dynamic behaviors of two systems, the order of the differential equations defining the system dynamics of the both systems are reduced by using the fact that the dynamic pressure changes in the hydraulic cylinder chambers become linearly dependent on leakage coefficients and cylinder chamber volumes above and below some prescribed cut off frequencies. Thus the open loop speed response of the pump controlled and valve controlled systems are defined by v second order transfer functions. The two systems are modeled in MATLAB Simulink environment and the assumptions are validated. For the position control of the single rod hydraulic actuator, a linear state feedback control scheme is applied. Its state feedback gains are determined by using the linear and linearized reduced order dynamic system equations. A linear Kalman filter for pump controlled system and an unscented Kalman filter for valve controlled system are designed for estimation and filtering purposes. The dynamic performances of both systems are investigated on an experimental test set up developed by conducting open loop and closed loop frequency response and step response tests. MATLAB Real Time Windows Target (RTWT) module is used in the tests for application purposes.

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.

Kamerabasierte Verfahren zur Steuerung autonomer mobiler Roboter wurden in den letzten Jahren immer populärer. In dieser Arbeit wird der Einsatz eines Stereokamerasystems und eines Mono-Kamera-SLAM Verfahrens hinsichtlich der Unterstützung der Navigation eines autonomen Luftschiffes untersucht. Mit Hilfe von Sensordaten aus IMU, GPS und Kamera wird eine Positionsschätzung über eine Sensorfusion mit Hilfe des Extended und des Unscented Kalman Filters durchgeführt.

Made available in DSpace on 2014-12-17T14:54:51Z (GMT). No. of bitstreams: 1 DiegoRCS.pdf: 1286138 bytes, checksum: 890aca771a97105bf912985058e417c2 (MD5) Previous issue date: 2008-12-11 / The present research aims at contributing to the area of detection and diagnosis of failure through the proposal of a new system architecture of detection and isolation of failures (FDI, Fault Detection and Isolation). The proposed architecture presents innovations related to the way the physical values monitored are linked to the FDI system and, as a consequence, the way the failures are detected, isolated and classified. A search for mathematical tools able to satisfy the objectives of the proposed architecture has pointed at the use of the Kalman Filter and its derivatives EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter). The use of the first one is efficient when the monitored process presents a linear relation among its physical values to be monitored and its out-put. The other two are proficient in case this dynamics is no-linear. After that, a short comparative of features and abilities in the context of failure detection concludes that the UFK system is a better alternative than the EKF one to compose the architecture of the FDI system proposed in case of processes of no-linear dynamics. The results shown in the end of the research refer to the linear and no-linear industrial processes. The efficiency of the proposed architecture may be observed since it has been applied to simulated and real processes. To conclude, the contributions of this thesis are found in the end of the text / O presente trabalho visa contribuir com a ?rea de detec??o e diagn?stico de falhas em sistemas din?micos atrav?s da proposta de uma nova arquitetura de sistemas de detec??o e isolamento de falhas (FDI, Fault Detection and Isolation). A arquitetura proposta traz inova??es no que se refere ? maneira como as grandezas f?sicas do processo monitorado s?o relacionadas ao sistema FDI e, em conseq??ncia disso, ? maneira como as falhas s?o detectadas, isoladas e classificadas. Uma busca por ferramentas matem?ticas capazes de satisfazer os objetivos da arquitetura proposta apontou para o uso do filtro de Kalman e seus derivados EKF (Extended Kalman Filter) e UKF (Unscented Kalman Filter). O uso do primeiro algoritmo mostra-se eficaz no caso em que o processo monitorado apresenta uma rela??o linear entre suas grandezas f?sicas a serem monitoradas e sua sa?da. Os outros dois, caso a din?mica seja n?o linear. Posteriormente, um comparativo entre o EKF e o UKF mostra que o segundo se adequa melhor ?s necessidades da arquitetura proposta. Os resultados mostrados no final da tese s?o referentes a plantas lineares e n?o-lineares, onde se pode observar a efic?cia da arquitetura proposta quando a mesma foi aplicada a processos simulados e reais

Kamerabasierte Verfahren zur Steuerung autonomer mobiler Roboter wurden in den letzten Jahren immer populärer. In dieser Arbeit wird der Einsatz eines Stereokamerasystems und eines Mono-Kamera-SLAM Verfahrens hinsichtlich der Unterstützung der Navigation eines autonomen Luftschiffes untersucht. Mit Hilfe von Sensordaten aus IMU, GPS und Kamera wird eine Positionsschätzung über eine Sensorfusion mit Hilfe des Extended und des Unscented Kalman Filters durchgeführt.