Algoritmos de busca do ponto de máxima potência (MPPT) são largamente utilizados para se obter a potência máxima fornecida em sistemas fotovoltaicos. Apesar de existirem diversas técnicas de MPPT, os métodos heurísticos de busca como o método da perturbação e observação (P&O) e o método da Condutância Incremental (InC) são os mais usuais por serem simples e confiáveis. Assim, é proposto uma lei de controle com realimentação da tensão de saída de um painel fotovoltaico (PV) de forma a manter o mesmo operando próximo ao ponto de máxima potência, possibilitando com isso uma melhora do desempenho do algoritmo de P&O e InC inclusive quando o painel PV é submetido a rápidas mudanças na irradiação solar. Além disso, devido às características não lineares dos painéis fotovoltaicos, a utilização de técnicas inteligentes que empregam controladores fuzzy para o controle da tensão terminal do PV, parece ser uma solução viável quando comparada aos controladores clássicos com PI e PID, principalmente, porque tais estruturas de controle proporcionam uma lenta resposta dinâmica e problemas de estabilidade relacionados às oscilações em torno do ponto de máxima potência, decorrente do funcionamento dos algoritmos P&O e InC. / Maximum power point tracking (MPPT) techniques are widely used to achieve the maximum output power of a photovoltaic (PV) system. Although there are many MPPT techniques, the heuristic search methods such as perturb and observe (P&O) and incremental conductance (InC) method are the most commonly used because they are simple and reliable in the pursuit of maximum power point. A control law with feedback of the output voltage of a photovoltaic (PV) panel is proposed, in order to maintain it working nearby the maximum power point, enabling an improvement in the performance of the algorithm for P&O and InC even when the PV array is subjected to rapid changes in solar irradiance. Moreover, due to the nonlinear characteristics of the photovoltaic panels, the technique that employ fuzzy logic control seems to be a viable option when compared with conventional proportional and integral or proportional, integral and derivative controllers, mainly because such control structures provide a slow dynamic response and stability issues related to oscillations around the maximum power point due to operation of P&O and InC algorithms.

Este trabalho teve por objetivo o desenvolvimento de um sistema de controle de navegação para um veículo autônomo utilizando lógica difusa. Atribui-se o nome de SNAT (Sistema de navegação-autônoma para um triciclo). O sistema é responsável pela automatização do processo de navegação de um veículo de testes monitorado e controlado de uma base remota mediante enlace de telemetria de dados. O usuário do sistema, pela tela principal da aplicação indica os pontos a seguir, cada um destes é referenciado pela sua latitude e longitude no mapa. Uma vez iniciada a navegação, o sistema de controle recebe periodicamente pacotes de dados da posição e direção do veículo; estes dados são processados pelo controlador difuso que gera os comandos para atuação sobre o veículo. O controlador difuso traduz as expressões qualitativas, comuns na comunicação humana em valores numéricos que representam a direção e a velocidade para manter ou corrigir a navegação do veículo de testes pelo rumo desejado. Nesta dissertação são apresentados diversos aspectos do desenvolvimento do controlador de navegação difusa, os mecanismos de comunicação e acionamento do veículo de uma base remota e os testes realizados que mostram os resultados do SNAT controlando automaticamente a navegação do veículo de testes. / This work had as a goal the development of a fuzzy logic based navigation control system for an autonomous vehicle. The developed control system was named SNAT (from the equivalent in portuguese of autonomous navigation system for a tricycle). The system controls and monitors the navigation of the vehicle from a remote base station using a telemetry data link. The user indicates, using a developed application, the navigation waypoints, referencing them by their latitude and longitude coordinates in a map. When the navigation starts, the system receives periodically data packets indicating the position and direction of the vehicle, these data are processed by the fuzzy control which returns commands to actuate over the vehicle. The fuzzy control translates qualitative expressions, common in human communication, into numerical values that represent the speed and the direction to keep the vehicle as near as possible to the desired navigation route. Many development aspects of the fuzzy controller and of the communication and actuation over the vehicle from the remote base are presented. Also are presented some results of the control system over navigation of the vehicle, which demonstrate that the system operates in a quite satisfactory manner.

Este trabalho teve por objetivo o desenvolvimento de um sistema de controle de navegação para um veículo autônomo utilizando lógica difusa. Atribui-se o nome de SNAT (Sistema de navegação-autônoma para um triciclo). O sistema é responsável pela automatização do processo de navegação de um veículo de testes monitorado e controlado de uma base remota mediante enlace de telemetria de dados. O usuário do sistema, pela tela principal da aplicação indica os pontos a seguir, cada um destes é referenciado pela sua latitude e longitude no mapa. Uma vez iniciada a navegação, o sistema de controle recebe periodicamente pacotes de dados da posição e direção do veículo; estes dados são processados pelo controlador difuso que gera os comandos para atuação sobre o veículo. O controlador difuso traduz as expressões qualitativas, comuns na comunicação humana em valores numéricos que representam a direção e a velocidade para manter ou corrigir a navegação do veículo de testes pelo rumo desejado. Nesta dissertação são apresentados diversos aspectos do desenvolvimento do controlador de navegação difusa, os mecanismos de comunicação e acionamento do veículo de uma base remota e os testes realizados que mostram os resultados do SNAT controlando automaticamente a navegação do veículo de testes. / This work had as a goal the development of a fuzzy logic based navigation control system for an autonomous vehicle. The developed control system was named SNAT (from the equivalent in portuguese of autonomous navigation system for a tricycle). The system controls and monitors the navigation of the vehicle from a remote base station using a telemetry data link. The user indicates, using a developed application, the navigation waypoints, referencing them by their latitude and longitude coordinates in a map. When the navigation starts, the system receives periodically data packets indicating the position and direction of the vehicle, these data are processed by the fuzzy control which returns commands to actuate over the vehicle. The fuzzy control translates qualitative expressions, common in human communication, into numerical values that represent the speed and the direction to keep the vehicle as near as possible to the desired navigation route. Many development aspects of the fuzzy controller and of the communication and actuation over the vehicle from the remote base are presented. Also are presented some results of the control system over navigation of the vehicle, which demonstrate that the system operates in a quite satisfactory manner.

The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.

This thesis deals with enhancing the performance of Fuzzy Logic (FL) based PI controllers for High Voltage Direct Current Transmission Systems (HVDC) by optimizing the key parameters i.e. membership functions (MFs) and fuzzy rule base in the controllers design. In the first part of the thesis, an adaptive Fuzzy PI controller is designed and the effect of various MF shapes, widths and distribution on the performance of a FL controlled HVDC system under different system conditions is studied with the aim of selecting a MF which minimizes the total control error. Simulated results show that the shape, width and distribution of a MF influences the performance of the FL controller and concludes that nonlinear MFs (i.e. Gaussian) offer a more better choice than linear (i.e. Triangular) MFs as the former provides a smoother transition at the switching points and thus propose a better controller. In the second part of the thesis, a Neuro-Fuzzy (NF) controller to update the fuzzy rule base with changing system conditions is proposed, which in turn adjusts the PI gains of a conventional PI controller. Results from simulations illustrate the potential of the proposed control scheme as the NF controller successfully adapts to different system conditions and is able to minimize the total current error. / UOIT

The aim of this work is to evaluate the fuzzy system for different types of patients for levodopa infusion in Parkinson Disease based on simulation experiments using the pharmacokinetic-pharmacodynamic model. Fuzzy system is to control patient’s condition by adjusting the value of flow rate, and it must be effective on three types of patients, there are three different types of patients, including sensitive, typical and tolerant patient; the sensitive patients are very sensitive to drug dosage, but the tolerant patients are resistant to drug dose, so it is important for controller to deal with dose increment and decrement to adapt different types of patients, such as sensitive and tolerant patients. Using the fuzzy system, three different types of patients can get useful control for simulating medication treatment, and controller will get good effect for patients, when the initial flow rate of infusion is in the small range of the approximate optimal value for the current patient’ type.

As time goes by rapid development of 3D graphics technique and 3C portable product output, 3D graphics have been widely applied to handheld devices, such as notebooks, PDAs, and smart cellular phones. Generally, to process 3D graphics applications in mobile devices, processor needs strong capability of handling large computational-intensive workloads. Complex computation consumes a great quantity of electric power. But the lifetime of handheld device battery is limited. Therefore, the cost, to satisfy this demand, will be shortening the supply time of device battery. Moreover, Moore¡¦ law said that the number of transistors in a chip is double in every eighteen months. But these days the advance in manufacturing batteries still cannot get up with the advance in developing processors. In addition, the improvement of chip size has led to more small, supply voltage of kernel processor in portable device. Considering system efficiency and battery lifetime simultaneously increase the difficulty of designing power management scheme. So, how to manage power effectively has become one of the important key for designing handheld products. For 3D graphics system, dynamic voltage and frequency scaling (DVFS) is one of good solutions to implement power management policy. DVFS needs an efficient online prediction method to predict the workload of frames and then appropriately adjust voltage and frequency for saving energy consumption. Consequently, a lot of related papers have proposed different prediction policy to predict the executing workload of 3D graphics system. For instance, the existing prediction policies include signature-based[1], history-based[3] and proportion-integral-derivative (PID)[14] methods, but most of designers put power management in software, i.e. processors. This solution not only slows power management to get the information about executing time of graphic processing unit (GPU), but also increases the operating overhead of CPU in handheld system. In this paper, we propose a power management workload prediction scheme with a framework of using proportion-integral (PI) controller to be a master controller and fuzzy controller to be a slave controller, and then implement it into hardware circuit. Taking advantage of fuzzy conception in fuzzy controller is to adjust the proportional parameter in PI controller, the shortage of traditional PI controller that demands on complicated try-and-error method to look for a good proportional and integral parameters can be avoided so that the adaption and forecasting accuracy can be improved. Besides, Uniform Window-size Predictor 1 (UW1) is also implemented as an assistant manner. Using UW1 predictor appropriately can improve the prediction trend to catch up with the trend of real workload. Experimental results show that our predictor improves prediction accuracy about 3.8% on average and saves about 0.02% more energy compared with PI predictor[18]. Circuit area and power consumption only increases 6.8% percent and 1.4% compared with PI predictor. Besides, we also apply our predictor to the 3D first person game, Quake II, in the market. The result shows that our predictor is indeed an effective prediction policy. The adaption can put up with the intense workload variation of real game and adjust voltage and frequency precisely to decrease power consumption and meet the purpose of energy saving.

In modern life, 3D graphics system is widely applied to portable product like Notebook, PDA and smart phone. Unlike desktop system, the capacity of batteries of these embedded systems is finite. Furthermore, rapid improvement of IC process leads to quick growth in the transistor count of a chip. According to above-mentioned reason and the complex computation of 3D graphics system, the power consumption will be very large. To efficiently lengthen the lifetime of battery, power management is an indispensable technique. Dynamic voltage and frequency scaling (DVFS) is one of the popular power management policy. In the scheme of DVFS, an accurate workload predictor is needed to predict the workload of every frame. According to these predictions a specific voltage and frequency level is applied to each frame of the 3D graphics system. The number of the voltage/frequency levels and the voltage/frequency of each level are fixed, the voltage/frequency table is decided according to the application of power management. Whenever the workload predictor completes the workload prediction of next frame, the voltage/frequency level of next frame will be found by looking up the voltage/frequency table. In this thesis, we propose a power management scheme with a framework composed of mainly Kalman filter and an auxiliary fuzzy controller to predict the workload of next frame. This scheme amends the shortcomings of traditional Kalman filter that needs to know the system features beforehand. And we propose a brand new concept named ¡¨delayed display¡¨ to massively reduce the miss rate of prediction without changing the framework of predictor.

Taipower Company has already completed the installation of the Outage Management System (OMS) in all service districts. The attributes of all distribution equipments and customers have been included in the computerized mapping system. However, the phasing attributes of distribution transformers are not very accurate in the database because they are very difficult to be identified for the distribution systems. The phase information of transformers in the OMS database is often inconsistent with the actual service phase, which deteriorates the performance of distribution system analysis, planning, and operation of Taipower distribution systems. The objective of this thesis is to develop an innovative Phasor Measurement Unit (PMU) to support the phasing identification of distribution transformers in a very effective way. The proposed PMU is used to measure the low voltage signal at the secondary side of transformers to prevent the survey personnel from safety problem. With the measured phases information of distribution transformers stored in the embedded system, the attributes of transformer phases in OMS can be updated to improve the accuracy of database. For the underground distribution systems, it is very difficult to receive the 1PPS signal of GPS system for timing synchronous to support the proposed transformer phasing measurement because all transformers are located at the building basement. This thesis proposes a timing synchronous module by using the Temperature Compensated Crystal Oscillator, TCXO to maintain the timing accuracy with high precision. Before executing the phasing measurement, this module is calibrated using the GPS 1PPS signal with fuzzy control calibration algorithm. It is found that the proposed PMU module can maintain the timing synchronous with 6˚, during two hours time period which will support the transformer phasing measurement by providing the reference timing synchronous even without the GPS 1PPS signal.

Controle de processos é uma das muitas aplicações que aproveitam as vantagens do uso da teoria de conjuntos nebulosos. Nesse tipo de aplicação, o controlador é, geralmente, embutido no dispositivo controlado. Esta dissertação propõe uma arquitetura reconfigurável eficiente para controladores nebulosos embutidos. A arquitetura é parametrizável, de tal forma, que permite a configuração do controlador para que este possa ser usado na implementação de qualquer aplicação ou modelo nebuloso. Os parâmetros de configuração são: o número de variáveis de entrada (N); o número de variáveis de saída (M); o número de termos linguísticos (Q); e o número total de regras (P). A arquitetura proposta proporciona também a configuração das características que definem as regras e as funções de pertinência de cada variável de entrada e saída, permitindo a escalabilidade do projeto. A composição das premissas e consequentes das regras são configuráveis, de acordo com o controlador nebuloso objetivado. A arquitetura suporta funções de pertinência triangulares, mas pode ser estendida para aceitar outras formas, do tipo trapezoidal, sem grandes modificações. As características das funções de pertinência de cada termo linguístico, podem ser ajustadas de acordo com a definição do controlador nebuloso, permitindo o uso de triângulos. Virtualmente, não há limites máximos do número de regras ou de termos linguísticos empregados no modelo, bem como no número de variáveis de entrada e de saída. A macro-arquitetura do controlador proposto é composta por N blocos de fuzzificação, 1 bloco de inferência, M blocos de defuzzificação e N blocos referentes às características das funções de pertinência. Este último opera apenas durante a configuração do controlador. A função dos blocos de fuzzificação das variáveis de entrada é executada em paralelo, assim como, os cálculos realizados pelos blocos de defuzzificação das variáveis de saída. A paralelização das unidades de fuzzificação e defuzzificação permite acelerar o processo de obtenção da resposta final do controlador. Foram realizadas várias simulações para verificar o correto funcionamento do controlador, especificado em VHDL. Em um segundo momento, para avaliar o desempenho da arquitetura, o controlador foi sintetizado em FPGA e testado em seis aplicações para verificar sua reconfigurabilidade e escalabilidade. Os resultados obtidos foram comparados com os do MATLAB em cada aplicação implementada, para comprovar precisão do controlador. / Process control is one of the many applications that benefits from fuzzy control. In this kind of application, the controller is usually embedded in the controlled device. This dissertation proposes a reconfigurable architecture for efficient embedded fuzzy controllers. The architecture is customizable, as it allows the controller configuration to be used to implement any fuzzy model. The configuration parameters are: the number of input variables (N); the number of output variables (M); the number of linguistic terms (Q); and the total number of rules (P). The proposed architecture also enables the configuration of the characteristics that define the rules and membership functions of each input and output variable, allowing for an optimal scalability of the project. The composition of the antecedent and consequent of the rules are configurable, according to the fuzzy model that is being implemented. A priori, the architecture supports triangular membership functions, but it can be extended to accommodate other forms, such as trapezium, without major modifications. The characteristics of the lines, forming the membership functions of the linguistic terms, can be adjusted according to the definition of the fuzzy model, allowing the use of non-isosceles and isosceles triangles. Virtually, there are no limits on the number of rules or linguistic terms used in the model, as well as the number of input and output variables. The macro-architecture of the proposed controller is composed of N fuzzification blocks, 1 inference block, M defuzzification blocks and N blocks to handle the characteristics of the membership functions. This block operates only during the controller setup. The work done by the fuzzification blocks of the input variables is executed in parallel, as well as the computation performed by the defuzzification blocks of the output variables. The duplication of the fuzzification and defuzzification blocks accelerates the process of yielding the final response of the controller. Several simulations were performed to verify the correct operation of the controller, which is specified in VHDL. In a second stage, to evaluate the controller performance, the architecture was synthesized into a FPGA and tested with six applications to verify the reconfigurability and scalability of the design. The results obtained were compared with the ones obtained from MATLAB for each of the implemented applications, to demonstrate the accuracy of the controller.