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Controlo difuso de um AGV
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Advisor(s)
Abstract(s)
Neste trabalho pretende-se introduzir os conceitos associados à lógica difusa no controlo
de sistemas, neste caso na área da robótica autónoma, onde é feito um enquadramento da
utilização de controladores difusos na mesma. Foi desenvolvido de raiz um AGV
(Autonomous Guided Vehicle) de modo a se implementar o controlador difuso, e testar o
desempenho do mesmo. Uma vez que se pretende de futuro realizar melhorias e/ou
evoluções optou-se por um sistema modular em que cada módulo é responsável por uma
determinada tarefa. Neste trabalho existem três módulos que são responsáveis pelo
controlo de velocidade, pela aquisição dos dados dos sensores e, por último, pelo
controlador difuso do sistema.
Após a implementação do controlador difuso, procedeu-se a testes para validar o sistema
onde foram recolhidos e registados os dados provenientes dos sensores durante o
funcionamento normal do robô. Este dados permitiram uma melhor análise do desempenho
do robô. Verifica-se que a lógica difusa permite obter uma maior suavidade na transição de
decisões, e que com o aumento do número de regras é possível tornar o sistema ainda mais
suave. Deste modo, verifica-se que a lógica difusa é uma ferramenta útil e funcional para o
controlo de aplicações. Como desvantagem surge a quantidade de dados associados à
implementação, tais como, os universos de discurso, as funções de pertença e as regras. Ao
se aumentar o número de regras de controlo do sistema existe também um aumento das
funções de pertença consideradas para cada variável linguística; este facto leva a um
aumento da memória necessária e da complexidade na implementação pela quantidade de
dados que têm de ser tratados. A maior dificuldade no projecto de um controlador difuso
encontra-se na definição das variáveis linguísticas através dos seus universos de discurso e
das suas funções de pertença, pois a definição destes pode não ser a mais adequada ao
contexto de controlo e torna-se necessário efectuar testes e, consequentemente,
modificações à definição das funções de pertença para melhorar o desempenho do sistema.
Todos os aspectos referidos são endereçados no desenvolvimento do AGV e os respectivos
resultados são apresentados e analisados.
This work introduces the concepts associated with fuzzy logic control systems, in this case applied to autonomous robotics, where was made a contextualization of fuzzy controllers in this area. An AGV (Autonomous Guided Vehicle) was developed from scratch in order to implement the fuzzy controller, and test its performance. In order to incorporate future improvements, it was adopted a modular structure where each module is responsible for a particular task. In this work there are three modules that are responsible for velocity control, the acquisition of sensor data, and by the implementation of the fuzzy controller. After implementing the fuzzy controller, the system was tested in order to validate the collected and recorded data from the sensors during normal operation of the robot. This data enabled the analysis of the performance of the robot. Fuzzy logic allows a smooth transition between the decisions, and increasing the number of rules the system becomes smoother. Thus, we conclude that fuzzy logic is a useful and functional tool for control applications. The disadvantage arises in the amount of data associated with the implementation, such as the universes of discourse, the membership functions and rules. When the number of control rules increases, there is also an increase of the membership functions for each linguistic variable considered, which leads to an increase in the required memory and complexity in the implementation because of the amount of data that must be addressed. The major difficulty in the design of a fuzzy controller is the definition of the linguistic variables through their universes of discourse and their membership functions, since the definition of these may not be the most appropriate to the context of control and it becomes necessary to make tests, and consequently, changes in the definition of the membership functions to improve the system performance. All these issues were addressed in the design of the AGV, and the obtained results are presented and analyzed.
This work introduces the concepts associated with fuzzy logic control systems, in this case applied to autonomous robotics, where was made a contextualization of fuzzy controllers in this area. An AGV (Autonomous Guided Vehicle) was developed from scratch in order to implement the fuzzy controller, and test its performance. In order to incorporate future improvements, it was adopted a modular structure where each module is responsible for a particular task. In this work there are three modules that are responsible for velocity control, the acquisition of sensor data, and by the implementation of the fuzzy controller. After implementing the fuzzy controller, the system was tested in order to validate the collected and recorded data from the sensors during normal operation of the robot. This data enabled the analysis of the performance of the robot. Fuzzy logic allows a smooth transition between the decisions, and increasing the number of rules the system becomes smoother. Thus, we conclude that fuzzy logic is a useful and functional tool for control applications. The disadvantage arises in the amount of data associated with the implementation, such as the universes of discourse, the membership functions and rules. When the number of control rules increases, there is also an increase of the membership functions for each linguistic variable considered, which leads to an increase in the required memory and complexity in the implementation because of the amount of data that must be addressed. The major difficulty in the design of a fuzzy controller is the definition of the linguistic variables through their universes of discourse and their membership functions, since the definition of these may not be the most appropriate to the context of control and it becomes necessary to make tests, and consequently, changes in the definition of the membership functions to improve the system performance. All these issues were addressed in the design of the AGV, and the obtained results are presented and analyzed.
Description
Keywords
Lógica difusa AGV Controlo difuso Controlo de velocidade Rede CAN Fuzzy logic Fuzzy Control Velocity control CAN network
Citation
Publisher
Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto