Para acompanhar o atual crescimento de demanda energética mundial, novas topologias de geradores estão sendo pesquisadas, estando nesse nicho o Gerador a Relutância Chaveado. Para seu correto funcionamento é necessário que técnicas de controle sejam empregadas para garantir níveis estáveis de tensão gerada mediante variações de velocidade e/ou carga. Portanto, o objetivo deste trabalho é apresentar uma metodologia de um controlador fuzzy da tensão gerada para a máquina em questão. Uma simulação em Matlab Simulink é apresentada para um sistema de geração de energia utilizando um gerador a relutância chaveado integrada com a malha de controle fuzzy. Resultados da dinâmica do funcionamento do controlador fuzzy são apresentados. O Controlador fuzzy proposto apresentou bom desempenho ao manter a tensão gerada em níveis desejáveis frente a distúrbios de carga e de variação de velocidade no eixo do gerador. Trata-se de um controlador robusto e versátil que garante estabilidade de tensão gerada mesmo com a operação do sistema com velocidade variável e/ou variação de carga. / Due to the growing demand of electric power energy, the engineering has to evolve by producing new efficient techniques and low cost equipment. Therefore, new electric power generator topologies have been studied, mainly switched reluctance generators due to their simple structure, reliability and low cost of fabrication. In order for a good operation of a switched reluctance generator, control techniques have to be applied to guarantee stable voltage levels under variable speed and load conditions. Hence, the objective of this work is to present a methodology based on fuzzy voltage controller for switched reluctance machine. Simulations are achieved in Matlab/Simulink for a power energy generation system using a switched reluctance generator with a fuzzy control loop. Results of the dynamic response of such controller are presented. The fuzzy controller could obtain good performance maintaining voltage levels in desired range. Therefore, the proposed controller showed to be robust, versatile and guarantee the voltage stability under speed and load variations.

Die zuverlässige Einhaltung von Lieferzusagen stellt ein wichtiges Kriterium bei der Auswahl der Teilnehmer eines auftragsbezogenen Produktionsnetzes dar. Für die objektive Bewertung der Lieferzuverlässigkeit der potenziellen Netzwerkteilnehmer bedarf es der Quantifizierung der relevanten Unsicherheiten integriert in einen allgemein gültigen Ansatz der Verfügbarkeitsprüfung. Die Arbeit stellt daraus resultierend Ansätze zur Berechnung der Unsicherheit vor. Durch die Quantifizierung der Unsicherheit innerhalb der Unternehmen ergibt sich zudem die Möglichkeit der flexiblen, situationsabhängigen Nutzung des für langfristige Rahmenverträge reservierten Sicherheitsbestandes zur Befriedigung kurzfristiger Anfragen. Diese Aufgabe unterstützt ein konfigurierbares Modell zur Entscheidungsunterstützung, das auf einem Neuro-Fuzzy-System basiert. Die Kennzahlen der Lieferzuverlässigkeit unterliegen einem dynamischen Verhalten während des Wertschöpfungsprozesses in dem auftragsbasierten Produktionsnetz. Durch die Integration dieser Kennzahlen in das Management dieses Prozesses ergibt sich die Möglichkeit, aus der Zunahme der Unsicherheit mögliche Störungen und deren Auswirkungen bereits vor ihrem Eintreten zu erfassen und im Rahmen eines präventiven Störungsmanagements zu agieren.

The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may constitute the rule-base in a fuzzy expert system. Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in - time is designed in order to make the use of fuzzy rules, to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data. The rule base of ANFIS unfolded in time contains temporal TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation learning algorithm is used. The system takes the multivariate data and the num- ber of lags needed which are the output of Fuzzy MAR in order to describe a variable and predicts the future behavior. Computer simulations are performed by using synthetic and real multivariate data and a benchmark problem (Gas Furnace Data) used in comparing neuro- fuzzy systems. The tests are performed in order to show how the system efficiently model and forecast the multivariate temporal data. Experimental results show that the proposed model achieves online learning and prediction on temporal data. The results are compared by other neuro-fuzzy systems, specifically ANFIS.

In autonomous navigation of mobile robots the dynamic environment is a source of problems. Because it is not possible to model all the possible conditions, the key point in the robot control is to design a system that is adaptable to different conditions and robust in dynamic environments. This study presents a reactive control system for a Khepera robot with the ability to navigate in a dynamic environment for reaching goal objects. The main motivation of this research is to design a robot control, which is robust to sensor errors and sudden changes and adaptable to different environments and conditions. Behavior based approach is used with taking the advantage of fuzzy reasoning in design. Experiments are made on Webots, which is a simulation environment for Khepera robot.

Esta dissertaçãoo investiga a utilização de Particle Swarm Optimization (PSO) para a obtenção automática de sistemas fuzzy do tipo Mamdani, tendo como insumo apenas as definições das variáveis do problema, seus domínios e a função objetivo. Neste trabalho utilizam-se algumas técnicas conhecidas na tentativa de minimizar a obtenção de sistemas fuzzy que não sejam coerentes. As principais técnicas usadas são o método de Wang e Mendell, chamado de WM, para auxiliar na obtenção de regras, e os conceitos de clusterização para obtenção das funções de pertinência. Na função de avaliação proposta, considera-se não somente a acurácia do sistema fuzzy, através da medida do erro, mas também a sua interpretabilidade, através da medida da compacidade, que consiste da quantidade de regras e funções membro, da distinguibilidade, que permite evitar que as funções membro não se confundam, e da completude, que permite avaliar que as funções membro abranjam o máximo do domínio. O propósito deste trabalho consiste no desenvolvimento de um algoritmo baseado em PSO, cuja função de avaliação congregue todos esses objetivos. Com parâmetros bem definidos, o algoritmo pode ser utilizado em diversos tipos de problemas sem qualquer alteração, tornando totalmente automática a obtenção de sistemas fuzzy. Com este intuito, o algoritmo proposto é testado utilizando alguns problemas pré-selecionados, que foram classificados em dois grupos, com base no tipo de função: contínua ou discreta. Nos testes com funções contínuas, são utilizados sistemas tridimensionais, com duas variáveis de entrada e uma de saída, enquanto nos testes com funções discretas são utilizados problemas de classificação, sendo um com quatro variáveis e outro com seis variáveis de entrada. Os resultados gerados pelo algoritmo proposto são comparados com aqueles obtidos em outros trabalhos. / This dissertation investigates the use of Particle Swarm Optimization (PSO) to allow automatic modeling of Mamdani fuzzy systems taking as input only the variable definitions, their respective domains and the objective function. This work uses several known techniques to avoid the consideration of invalid fuzzy systems. The main used techniques are the WM method, which is used to generate rules, and the clustering concept, which assists in the generation of the membership functions. The evaluation function proposed considers not only the accuracy of the generated fuzzy system, but also the properties of interpretability and distinguishability. The accuracy of the fuzzy system is measured using the underlaying error. The system interpretability is evaluated using a compactness measure, which consists mainly of the number of employed rules and membership functions, while its distinguishability is quantified using the completeness measure, which consists of measuring how the used membership functions are covering the corresponding domain. The main goal of this work is to develop a PSO-based algorithm that uses a fitness function which congregates all these objectives. With well-defined parameters, the algorithm can be used with different kinds of problems without any change, allowing for a fully automatic generation process of an adequate fuzzy system. In this purpose, the proposed algorithm is tested for some benchmark problems, which are classified in two groups, based on the type of function to be modeled by the yield fuzzy system: completely or partially defined function. In the cases for fully-defined functions, three-dimensional functions are used. These functions have two input variables and one output variable. In the cases for partially-defined functions, two classification problems are used, one having four variables and other six input variables. The results obtained by the proposed algorithm are compared to related work.

Esta dissertaçãoo investiga a utilização de Particle Swarm Optimization (PSO) para a obtenção automática de sistemas fuzzy do tipo Mamdani, tendo como insumo apenas as definições das variáveis do problema, seus domínios e a função objetivo. Neste trabalho utilizam-se algumas técnicas conhecidas na tentativa de minimizar a obtenção de sistemas fuzzy que não sejam coerentes. As principais técnicas usadas são o método de Wang e Mendell, chamado de WM, para auxiliar na obtenção de regras, e os conceitos de clusterização para obtenção das funções de pertinência. Na função de avaliação proposta, considera-se não somente a acurácia do sistema fuzzy, através da medida do erro, mas também a sua interpretabilidade, através da medida da compacidade, que consiste da quantidade de regras e funções membro, da distinguibilidade, que permite evitar que as funções membro não se confundam, e da completude, que permite avaliar que as funções membro abranjam o máximo do domínio. O propósito deste trabalho consiste no desenvolvimento de um algoritmo baseado em PSO, cuja função de avaliação congregue todos esses objetivos. Com parâmetros bem definidos, o algoritmo pode ser utilizado em diversos tipos de problemas sem qualquer alteração, tornando totalmente automática a obtenção de sistemas fuzzy. Com este intuito, o algoritmo proposto é testado utilizando alguns problemas pré-selecionados, que foram classificados em dois grupos, com base no tipo de função: contínua ou discreta. Nos testes com funções contínuas, são utilizados sistemas tridimensionais, com duas variáveis de entrada e uma de saída, enquanto nos testes com funções discretas são utilizados problemas de classificação, sendo um com quatro variáveis e outro com seis variáveis de entrada. Os resultados gerados pelo algoritmo proposto são comparados com aqueles obtidos em outros trabalhos. / This dissertation investigates the use of Particle Swarm Optimization (PSO) to allow automatic modeling of Mamdani fuzzy systems taking as input only the variable definitions, their respective domains and the objective function. This work uses several known techniques to avoid the consideration of invalid fuzzy systems. The main used techniques are the WM method, which is used to generate rules, and the clustering concept, which assists in the generation of the membership functions. The evaluation function proposed considers not only the accuracy of the generated fuzzy system, but also the properties of interpretability and distinguishability. The accuracy of the fuzzy system is measured using the underlaying error. The system interpretability is evaluated using a compactness measure, which consists mainly of the number of employed rules and membership functions, while its distinguishability is quantified using the completeness measure, which consists of measuring how the used membership functions are covering the corresponding domain. The main goal of this work is to develop a PSO-based algorithm that uses a fitness function which congregates all these objectives. With well-defined parameters, the algorithm can be used with different kinds of problems without any change, allowing for a fully automatic generation process of an adequate fuzzy system. In this purpose, the proposed algorithm is tested for some benchmark problems, which are classified in two groups, based on the type of function to be modeled by the yield fuzzy system: completely or partially defined function. In the cases for fully-defined functions, three-dimensional functions are used. These functions have two input variables and one output variable. In the cases for partially-defined functions, two classification problems are used, one having four variables and other six input variables. The results obtained by the proposed algorithm are compared to related work.

Master’s thesis deals with the evaluation and selection of suppliers of products and services for the company Momedica, s.r.o. using fuzzy logic. The resulting fuzzy system will serve as a decision support instrument of company.

yes / In modern chemical plants, it is essential to establish an effective maintenance strategy which will deliver financially driven results at optimised conditions, that is, minimum cost and time, by means of a criticality review of equipment in maintenance. In this article, a fuzzy logic-based criticality assessment system (FCAS) for the management of a local company’s equipment maintenance is introduced. This fuzzy system is shown to improve the conventional crisp criticality assessment system (CCAS). Results from case studies show that not only can the fuzzy logic-based system do what the conventional crisp system does but also it can output more criticality classifications with an improved reliability and a greater number of different ratings that account for fuzziness and individual voice of the decision-makers.

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Increasing demands, shrinking financial and human resources, and increased infrastructure deterioration have made the task of maintaining the infrastructure systems more challenging than ever before. Life-cycle cost analysis (LCCA) is an important tool for transportation infrastructure management, which is used extensively to support project level decisions, and is increasingly being applied to enhance network level analysis. However, traditional LCCA tools cannot practically and effectively utilize expert knowledge and handle ambiguous uncertainties. The main objective of this dissertation was to develop enhanced LCCA models using soft computing (mainly fuzzy logic) techniques. The proposed models use available "real-world" information to forecast life-cycle costs of competing maintenance and rehabilitation strategies and support infrastructure management decisions. A critical review of available soft computing techniques and their applications in infrastructure management suggested that these techniques provide appealing alternatives for supporting many of the infrastructure management functions. In particular, LCCA often utilizes information that is uncertain, ambiguous and incomplete, which is obtained from both existing databases and expert opinion. Consequently, fuzzy logic techniques were selected to enhance life-cycle cost analysis of transportation infrastructure investments because they provide a formal approach for the effective treatment of these types of information. The dissertation first proposes a fuzzy-logic-based decision-support model, whose inference rules can be customized according to agency's management policies and expert opinion. The feasibility and practicality of the proposed model is illustrated by its implementation in a life-cycle cost analysis algorithm for comparing and selecting pavement maintenance, rehabilitation and reconstruction (MR&R) policies. To enhance the traditional probabilistic LCCA model, the fuzzy-logic-based model is then incorporated into the risk analysis process. A fuzzy logic approach for determining the timing of pavement MR&R treatments in a probabilistic LCCA model for selecting pavement MR&R strategies is proposed. The proposed approach uses performance curves and fuzzy-logic triggering models to determine the most effective timing of pavement MR&R activities. The application of the approach in a case study demonstrates that the fuzzy-logic-based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the ambiguous uncertainty inherent to the process. Finally, the research establishes a systematic method to calibrate the fuzzy-logic based rehabilitation decision model using real cases extracted from the Long Term Pavement Performance (LTPP) database. By reinterpreting the model in the form of a neuro-fuzzy system, the calibration algorithm takes advantage of the learning capabilities of artificial neural networks for tuning the fuzzy membership functions and rules. The practicality of the method is demonstrated by successfully tuning the treatment selection model to distinguish between rehabilitation (light overlay) and do-nothing cases. / Ph. D.