Submitted by Sandro Camargo (sandro.camargo@unipampa.edu.br) on 2015-05-09T20:45:31Z No. of bitstreams: 1 117110026.pdf: 2752105 bytes, checksum: bd6b8c2341ad7ebc5b553fad3338b56f (MD5) / Made available in DSpace on 2015-05-09T20:45:31Z (GMT). No. of bitstreams: 1 117110026.pdf: 2752105 bytes, checksum: bd6b8c2341ad7ebc5b553fad3338b56f (MD5) Previous issue date: 2014-12-01 / O controle de conversores eletrônicos de potência tem recebido muita atenção devido às suas inúmeras aplicações. Destacam-se especialmente aplicações em problemas de qualidade de energia, onde é necessário injetar uma corrente na rede elétrica de acordo com uma referência. A conexão de conversores na rede elétrica, no entanto, apresenta diversos desafios, como a existência de incerteza paramétrica na planta e distúrbios advindos da rede. Além disso, inerentemente ao seu funcionamento, conversores eletrônicos de potência geram componentes harmônicas de comutação que precisam ser filtradas. A tendência atual das estratégias de controle é o relaxamento da exigência clássica de conhecimento completo da planta a ser controlada, buscando robustez com relação às incertezas paramétricas. Este trabalho apresenta uma estratégia de controle capaz de rejeitar distúrbios e apresentar bom desempenho frente a incertezas, utilizando técnicas de controle multimalhas e controle adaptativo. Por fim, são apresentados resultados de simulação, e resultados experimentais que mostram o bom funcionamento do sistema. / Voltage-source converter control is being very exploited due to its numerous applications. Special attention is given to energy quality applications, which demand the injection of currents in the grid according to a reference current. The connection of converters to the grid, however, presents several challenges such as parametric uncertainty associated to the plant and disturbances coming from the grid. Furthermore, inverters generate switching harmonics that need to be filtered. The tendency in control strategies is the relaxation of the classical requirement of complete knowledge of the plant, seeking robustness with respect to parametric uncertainties. This work presents a control strategy capable of disturbance rejection and good performance in relation to uncertainties, using Multi-Loop and Adaptive control techniques. Simulation results are presented, and experimental results show the good behavior of the proposed system.

Made available in DSpace on 2014-12-17T14:55:44Z (GMT). No. of bitstreams: 1 HumbertoAS_DISSERT.pdf: 1011716 bytes, checksum: 2b872372fd085e114502d8002132d6ba (MD5) Previous issue date: 2010-03-19 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / Postsurgical complication of hypertension may occur in cardiac patients. To decrease the chances of complication it is necessary to reduce elevated blood pressure as soon as possible. Continuous infusion of vasodilator drugs, such as sodium nitroprusside (Nipride), would quickly lower the blood pressure in most patients. However, each patient has a different sensitivity to infusion of Nipride. The parameters and the time delays of the system are initially unknown. Moreover, the parameters of the transfer function associated with a particular patient are time varying. the objective of the study is to develop a procedure for blood pressure control i the presence of uncertainty of parameters and considerable time delays. So, a methodology was developed multi-model, and for each such model a Preditive Controller can be a priori designed. An adaptive mechanism is then needed for deciding which controller should be dominant for a given plant / Em muitos procedimentos cir?rgicos existe a necessidade de realizar o controle da press?o arterial para, com isto, preservar a sa?de do paciente. Para diminuir as chances de uma complica??o, ? necess?rio reduzir a press?o arterial o mais r?pido poss?vel. A infus?o cont?nua de drogas vasodilatadoras, como o nitroprussiato de s?dio (NPS), reduz rapidamente a press?o arterial na maioria dos pacientes. Por?m, cada paciente tem uma sensibilidade diferente a infus?o do NPS, o que faz com que os par?metros e os atrasos do sistema sejam desconhecidos a priori. Al?m disso, os par?metros de uma fun??o de transfer?ncia associados ? um paciente particular s?o variantes no tempo. Desta forma, o objetivo do trabalho consiste em desenvolver uma metodologia capaz de controlar de forma autom?tica a press?o arterial na presen?a de incertezas de par?metros e de grandes atrasos. Para isso foi desenvolvida uma metodologia multi-modelo, onde para cada modelo existe um Controlador Preditivo especificamente sintonizado, e um mecanismo adaptativo decide qual controlador deve ser o dominante para uma determinada planta

Modeling and simulation have become fully ingrained into the set of design and development tools that are broadly used in the field of power electronics. To state simply, they represent the fastest and safest way to study a circuit or system, thus aiding in the research, design, diagnosis, and debugging phases of power converter development. Advances in computing technologies have also enabled the ability to conduct reliability and production yield analyses to ensure that the system performance can meet given requirements despite the presence of inevitable manufacturing variability and variations in the operating conditions. However, the trustworthiness of all the model-based design techniques depends entirely on the accuracy of the simulation models used, which, thus far, has not yet been fully considered. Prior to this research, heuristic safety factors were used to compensate for deviation of real system performance from the predictions made using modeling and simulation. This approach resulted invariably in a more conservative design process. In this research, a modeling and design approach with parametric and model-form uncertainty quantification is formulated to bridge the modeling and simulation accuracy and reliance gaps that have hindered the full exploitation of model-based design techniques. Prior to this research, a few design approaches were developed to account for variability in the design process; these approaches have not shown the capability to be applicable to complex systems. This research, however, demonstrates that the implementation of the proposed modeling approach is able to handle complex power converters and systems. A systematic study for developing a simplified test bed for uncertainty quantification analysis is introduced accordingly. For illustrative purposes, the proposed modeling approach is applied to the switching model of a modular multilevel converter to improve the existing modeling practice and validate the model used in the design of this large-scale power converter. The proposed modeling and design methodology is also extended to design optimization, where a robust multi-objective design and optimization approach with parametric and model form uncertainty quantification is proposed. A sensitivity index is defined accordingly as a quantitative measure of system design robustness, with regards to manufacturing variability and modeling inaccuracies in the design of systems with multiple performance functions. The optimum design solution is realized by exploring the Pareto Front of the enhanced performance space, where the model-form error associated with each design is used to modify the estimated performance measures. The parametric sensitivity of each design point is also considered to discern between cases and help identify the most parametrically-robust of the Pareto-optimal design solutions. To demonstrate the benefits of incorporating uncertainty quantification analysis into the design optimization from a more practical standpoint, a Vienna-type rectifier is used as a case study to compare the theoretical analysis with a comprehensive experimental validation. This research shows that the model-form error and sensitivity of each design point can potentially change the performance space and the resultant Pareto Front. As a result, ignoring these main sources of uncertainty in the design will result in incorrect decision-making and the choice of a design that is not an optimum design solution in practice. / Ph. D.

Esta tese apresenta novos algoritmos que resolvem problemas de estimativas filtrada, suavizadora e preditora para sistemas singulares no tempo discreto usando apenas argumentos determinísticos. Cada capítulo aborda inicialmente as estimativas para o sistema nominal e em seguida, as versões robustas para o sistema com incertezas limitadas. Os resultados encontrados podem ser aplicados tanto em sistemas invariantes como variantes no tempo discreto, utilizando a mesma estrutura do filtro de Kalman. Nos últimos anos, uma quantidade significativa de trabalhos envolvendo estimativas singulares foi publicada enfocando apenas a estimativa filtrada sob a justificativa de que a estimativa preditora era de significativa complexidade quando modelada pelo método dos mínimos quadrados. Por este motivo, poucos trabalhos, como NIKOUKHAH et al. (1992) e ZHANG et al. (1998), deduziram a estimativa preditora. Este último artigo apresentou também um algoritmo para a estimativa suavizadora, mas usando o modelo de inovação ARMA. No entanto, até onde foi possível identificar, nenhum trabalho até agora resolveu o problema de estimativa robusta, considerando incertezas nos parâmetros, para sistemas singulares. Para a dedução das estimativas singulares robustas, esta tese tomou como base SAYED (2001), que deduz o filtro de Kalman robusto com incertezas limitadas utilizando uma abordagem determinística, o chamado filtro BDU. Os filtros robustos para sistemas singulares apresentados nesta tese, são mais abrangentes que os apresentados em SAYED (2001). Quando particularizados para o espaço de estados sem incertezas, todos os filtros se assemelham ao filtro de Kalman. / New algorithms to optimal recursive filtering, smoothed and prediction for general time-invariant or time-variant descriptor systems are proposed in this thesis. The estimation problem is addressed as an optimal deterministic trajectory fitting. This problem is solved using exclusively deterministic arguments for systems with or without uncertainties. Kalman type recursive algorithms for robust filtered, predicted and smoothed estimations are derived. In the last years, many papers have paid attention to the estimation problems of linear singular systems. Unfortunately, all those works were concentrated only on the study of filtering problems, for nominal systems. The predicted and smoothed filters are more involved and were considered only by few works : NIKOUKHAH et al. (1992) and ZHANG et al. (1998) had proposed a unified approach for filtering, prediction and smoothing problems which were derived by using the projection formula and were calculated based on the ARMA innovation model, but they had not considered the uncertainties. In this thesis its applied for descriptor systems a robust procedure for usual state space systems developed by SAYED (2001), called BDU filter. It is obtained a robust descriptor Kalman type recursions for filtered, predicted and smoothed estimates. Considering the nominal state space, all descriptor filters developed in this work collapse to the Kalman filter.

Esta tese apresenta novos algoritmos que resolvem problemas de estimativas filtrada, suavizadora e preditora para sistemas singulares no tempo discreto usando apenas argumentos determinísticos. Cada capítulo aborda inicialmente as estimativas para o sistema nominal e em seguida, as versões robustas para o sistema com incertezas limitadas. Os resultados encontrados podem ser aplicados tanto em sistemas invariantes como variantes no tempo discreto, utilizando a mesma estrutura do filtro de Kalman. Nos últimos anos, uma quantidade significativa de trabalhos envolvendo estimativas singulares foi publicada enfocando apenas a estimativa filtrada sob a justificativa de que a estimativa preditora era de significativa complexidade quando modelada pelo método dos mínimos quadrados. Por este motivo, poucos trabalhos, como NIKOUKHAH et al. (1992) e ZHANG et al. (1998), deduziram a estimativa preditora. Este último artigo apresentou também um algoritmo para a estimativa suavizadora, mas usando o modelo de inovação ARMA. No entanto, até onde foi possível identificar, nenhum trabalho até agora resolveu o problema de estimativa robusta, considerando incertezas nos parâmetros, para sistemas singulares. Para a dedução das estimativas singulares robustas, esta tese tomou como base SAYED (2001), que deduz o filtro de Kalman robusto com incertezas limitadas utilizando uma abordagem determinística, o chamado filtro BDU. Os filtros robustos para sistemas singulares apresentados nesta tese, são mais abrangentes que os apresentados em SAYED (2001). Quando particularizados para o espaço de estados sem incertezas, todos os filtros se assemelham ao filtro de Kalman. / New algorithms to optimal recursive filtering, smoothed and prediction for general time-invariant or time-variant descriptor systems are proposed in this thesis. The estimation problem is addressed as an optimal deterministic trajectory fitting. This problem is solved using exclusively deterministic arguments for systems with or without uncertainties. Kalman type recursive algorithms for robust filtered, predicted and smoothed estimations are derived. In the last years, many papers have paid attention to the estimation problems of linear singular systems. Unfortunately, all those works were concentrated only on the study of filtering problems, for nominal systems. The predicted and smoothed filters are more involved and were considered only by few works : NIKOUKHAH et al. (1992) and ZHANG et al. (1998) had proposed a unified approach for filtering, prediction and smoothing problems which were derived by using the projection formula and were calculated based on the ARMA innovation model, but they had not considered the uncertainties. In this thesis its applied for descriptor systems a robust procedure for usual state space systems developed by SAYED (2001), called BDU filter. It is obtained a robust descriptor Kalman type recursions for filtered, predicted and smoothed estimates. Considering the nominal state space, all descriptor filters developed in this work collapse to the Kalman filter.