The speed control of a Brushless DC Motor Drive in the presence of load disturbance is investigated. Firstly some practical results are presented where a simple proportional-integral speed controller is used in the presence of a large step input speed demand as well as load disturbance. The wind-up problem caused by the saturation of the controller is discussed. In order to improve the performance of the proportional-integral speed controller in the presence of load variation, a load estimator is used with torque feedforward control. The results presented show the speed holding capability in the presence of load variation is significantly improved. A genetic algorithm is used on line to optimise the controller for different conditions such as large and small step input speed demand and load disturbance. The results presented show that a genetic algorithm is capable of finding the tuning of the controller for optimal performance. Single-input single-output and two-input two-output fuzzy speed controllers are also used and the results compared to a proportional-integral controller. Results are presented showing that a single-input single-output fuzzy controller works as a proportional controller with variable gain whereas the two-input two-output fuzzy controller is capable of driving the motor at variable speed and load torque with excellent performance. The robustness of the fuzzy controllers is compared to the proportional-integral controller and the results presented show that the fuzzy one is more robust then the proportional-integral. A genetic algorithm is also used on line for the optimisation of the two-input twooutput fuzzy speed controller and the results show that despite the large number of parameters to be optimised, the tuning for optimal performance is also possible.

Made available in DSpace on 2016-06-02T19:05:50Z (GMT). No. of bitstreams: 1 3678.pdf: 5394148 bytes, checksum: 4abd3e7ce4acac9d5890eae3620c06d3 (MD5) Previous issue date: 2011-06-14 / Financiadora de Estudos e Projetos / In recent years, most companies apply techniques of industrial automation with the goal of increasing its efficiency. Increasingly Automated Guided Vehicles (AGVs) are used to transport work in factories and warehouses. The management of these AGV is the key to an efficient transport system. One of the main problems encountered in the management of AGV is the dispatching decision. This work proposes a method of dispatching of AGV with the ability to optimize the performance of flexible manufacturing systems (FMS) in real time. In the dispatching deciding can be chosen to optimize two objectives: makespan or tardiness. When the goal is to optimize the makespan are evaluated variables: distance, number of nodes, the input buffer and output buffer of the workstation. When the goal is to optimize the tardiness are evaluated variables: distance, number of nodes, task chaining, date of delivery. In both cases are also considered the time of the AGVs and the processing times of the workstations, so you can anticipate some decision-making. To perform the tests we used the softwares: Matlab, CPNtools and Automod, which allow simulations of how the method works dispatching AGVs before the method is implemented in factories. Based on the tests, we analyzed the improvement in makespan and tardiness compared to other studies, but also the flexibility of the method in which it is possible to exchange goals in operating time. / Nos últimos anos, mais empresas aplicam técnicas de automação industrial com o objetivo de aumentar sua eficiência. Cada vez mais Veículos Auto Guiados (AGVs) são usados para tarefa de transporte em indústrias e armazéns. O gerenciamento desses AGVs é a chave para um sistema de transporte eficiente. Um dos principais problemas encontrados no gerenciamento dos AGVs é a decisão de despacho. Esse trabalho propõe um método de despacho de AGV com capacidade de otimizar o desempenho de Sistemas Flexíveis de Manufatura (FMS) em tempo real. Na tomada de decisão do despacho podem ser escolhidos dois objetivos para otimização: makespan ou tardiness. Quando o objetivo é otimizar o makespan são avaliadas as variáveis: distância, número de nós, buffer de entrada e buffer de saída das estações de trabalho. Quando o objetivo é otimizar o tardiness são avaliadas as variáveis: distância, número de nós, encadeamento de tarefas, data de entrega do produto. Em ambos os casos também são considerados os tempos dos AGVs e os tempos de processamento das estações de trabalho, para que seja possível adiantar algumas tomadas de decisões. Para realizar os testes foram usados os softwares MatLab, CPNtools e Automod, os quais permitem que simulações do funcionamento do método de despacho de AGVs antes que o método seja implementado em fábricas. Com base nos testes realizados, analisou-se as melhoras no makespan e tardiness em comparação com outros trabalhos, como também a flexibilidade do método no qual é possível a troca de objetivos em tempo de operação.