Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work presents a contribution to the study of AC Power Sources, where a prototype s development is presented. The stages that compose the system, including converter topology, filter, instrumentation and controller, are presented. The developed prototype is a three-phase four-wire source, which uses a four-leg voltage source inverter. This topology increases the flexibility for unbalanced waveforms generation or unbalanced load conditions, also simplifying the control problem of the process. This work first looks for justifying the use and the study of AC Power Sources, in this sense, some examples of tests and norms, whose tests demand its use, are given. The applications are in general related to electrical and electronic equipments and for driving electromechanical plants such as shakers. In this sense, AC Power Sources are equipment that can be used both in industry applications and didactic or research laboratories. In practically all applications, it is shown that good performance in waveforms generation is necessary. This performance is related to the ability for waveforms generation with low harmonic distortion even in conditions of variable frequency or amplitude and with nonlinear loads behavior. In this way, the use of an adequate converter topology is not enough, are too necessary controllers to guarantee performance for the system, even in adverse load conditions or in presence of unmodeled dynamics. The unmodeled dynamics are derived from some stages that compose the system, but they are generally related to the uncertainties on the model of the plant and load. Then, this work not only describes the implemented prototype and topological relative questions but also applies a Robust Model Reference Adaptive Control (RMRAC) for the plant control. This technique improves the robustness in the closed loop system even under presence of unmodeled dynamics and disturbances. The controller makes use of a Gradient type algorithm for parametric adaptation with four adapted parameters, which leads to a new error equation that is used for the controller s implementation / Este trabalho apresenta uma contribuição ao estudo de Fontes de Potência CA onde é apresentado o desenvolvimento de um protótipo. Os diversos estágios que compõem o sistema, desde a topologia do conversor, do filtro, da instrumentação e do controlador são apresentados e analisados. O protótipo desenvolvido é uma fonte trifásica a quatro fios, que utiliza um inversor de tensão de quatro braços. Esta topologia permite uma maior flexibilidade na geração de formas de onda desbalanceadas ou em condições de cargas desequilibradas, simplificando também o problema de controle do sistema. Este trabalho procura primeiramente justificar a utilização e o estudo de Fontes de Potência CA, neste sentido são dados alguns exemplos de ensaios e normas, cujos testes demandam a sua utilização. As aplicações são em geral relacionadas a equipamentos eletroeletrônicos e acionamento de outras plantas como vibradores eletromecânicos. Deste modo, as Fontes de Potência CA são equipamentos que podem ser utilizados tanto na indústria quanto em laboratórios didáticos e de pesquisa. Em praticamente todas as aplicações, é mostrado que um bom desempenho na geração de formas de onda é necessário. Este desempenho está relacionado à capacidade de geração de formas de onda com baixa distorção harmônica, não raro, em condições de freqüência e amplitude variáveis e com cargas de comportamento não-linear. Deste modo, a seleção de uma topologia de conversor adequado não basta, são também necessários controladores que garantam um bom desempenho do sistema, mesmo em condições adversas de carga e em presença de dinâmicas não-modeladas. As dinâmicas não-modeladas são oriundas das várias etapas que compõem o sistema, mas geralmente são relacionadas às incertezas sobre o modelo da planta e da carga. Neste sentido, este trabalho descreve não apenas o protótipo implementado e questões topológicas relativas a ele, mas também aplica um Controle Robusto por Modelo de Referência ou RMRAC (Robust Model Reference Adaptive Control) para o controle da planta. Esta técnica garante robustez do sistema em malha fechada mesmo na presença de dinâmicas não modeladas e distúrbios. O controlador utilizado faz uso de um algoritmo de adaptação paramétrica do tipo Gradiente, no qual quatro parâmetros são adaptados. Este fato leva a uma nova equação do erro, que é utilizada para a implementação do controlador.

En théorie de la commande, un modèle du système est généralement utilisé pour construire la loi de commande et assurer ses performances. Les équations mathématiques qui représentent le système à contrôler sont utilisées pour assurer que le contrôleur associé va stabiliser la boucle fermée. Mais, en pratique, le système réel s’écarte du comportement théorique modélisé. Des non-linéarités ou des dynamiques rapides peuvent être négligées, les paramètres sont parfois difficiles à estimer, des perturbations non maitrisables restent non modélisées. L’approche proposée dans ce travail repose en partie sur la connaissance du système à piloter par l’utilisation d’un modèle analytique mais aussi sur l’utilisation de données expérimentales hors ligne ou en ligne. A chaque pas de temps la valeur de la commande qui amène au mieux le système vers un objectif choisi a priori, est le résultat d’un algorithme qui minimise une fonction de coût ou maximise une récompense. Au centre de la technique développée, il y a l’utilisation d’un modèle numérique de comportement du système qui se présente sous la forme d’une fonction de prédiction tabulée ayant en entrée un n-uplet de l’espace joint entrées/état ou entrées/sorties du système. Cette base de connaissance permet l’extraction d’une sous-partie de l’ensemble des possibilités des valeurs prédites à partir d’une sous-partie du vecteur d’entrée de la table. Par exemple, pour une valeur de l’état, on pourra obtenir toutes les possibilités d’états futurs à un pas de temps, fonction des valeurs applicables de commande. Basé sur des travaux antérieurs ayant montré la viabilité du concept en entrées/état, de nouveaux développements ont été proposés. Le modèle de prédiction est initialisé en utilisant au mieux la connaissance a priori du système. Il est ensuite amélioré par un algorithme d’apprentissage simple basé sur l’erreur entre données mesurées et données prédites. Deux approches sont utilisées : la première est basée sur le modèle d’état (comme dans les travaux antérieurs mais appliquée à des systèmes plus complexes), la deuxième est basée sur un modèle entrée-sortie. La valeur de commande qui permet de rapprocher au mieux la sortie prédite dans l’ensemble des possibilités atteignables de la sortie ou de l’état désiré, est trouvée par un algorithme d’optimisation. Afin de valider les différents éléments proposés, cette commande a été mise en œuvre sur différentes applications. Une expérimentation réelle sur un quadricoptère et des essais réels de suivi de trajectoire sur un véhicule électrique du laboratoire montrent sacapacité et son efficacité sur des systèmes complexes et rapides. D’autres résultats en simulation permettent d’élargir l’étude de ses performances. Dans le cadre d’un projet partenarial, l’algorithme a également montré sa capacité à servir d’estimateur d’état dans la reconstruction de la vitesse mécanique d’une machine asynchrone à partir des signaux électriques. Pour cela, la vitesse mécanique a été considérée comme l’entrée du système. / In classical control theory, the control law is generally built, based on the theoretical model of the system. That means that the mathematical equations representing the system dynamics are used to stabilize the closed loop. But in practice, the actual system differs from the theory, for example, the nonlinearity, the varied parameters and the unknown disturbances of the system. The proposed approach in this work is based on the knowledge of the plant system by using not only the analytical model but also the experimental data. The input values stabilizing the system on open loop, that minimize a cost function, for example, the distance between the desired output and the predicted output, or maximize a reward function are calculated by an optimal algorithm. The key idea of this approach is to use a numerical behavior model of the system as a prediction function on the joint state and input spaces or input-output spaces to find the controller’s output. To do this, a new non-linear control concept is proposed, based on an existing controller that uses a prediction map built on the state-space. The prediction model is initialized by using the best knowledge a priori of the system. It is then improved by using a learning algorithm based on the sensors’ data. Two types of prediction map are employed: the first one is based on the state-space model; the second one is represented by an input-output model. The output of the controller, that minimizes the error between the predicted output from the prediction model and the desired output, will be found using optimal algorithm. The application of the proposed controller has been made on various systems. Some real experiments for quadricopter, some actual tests for the electrical vehicle Zoé show its ability and efficiency to complex and fast systems. Other the results in simulation are tested in order to investigate and study the performance of the proposed controller. This approach is also used to estimate the rotor speed of the induction machine by considering the rotor speed as the input of the system.