Estratégia de modelagem para controle de FMS combinando redes de Petri e um sistema Fuzzy

Detalhes bibliográficos
Ano de defesa: 2012
Autor(a) principal: Reis, Felipe Bezerra dos
Orientador(a): Morandin Júnior, Orides lattes
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de São Carlos
Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
Departamento: Não Informado pela instituição
País: BR
Palavras-chave em Português:
Palavras-chave em Inglês:
FMS
Área do conhecimento CNPq:
Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/584
Resumo: The flexible manufacturing systems (FMS) are automated production systems capable of producing a wide variety of product types. They are composed by machines, robots and automated transport systems. Make the control of an FMS is a complex task due to the various subsystems and elements that compose it, and also by the necessity of respond to strategic issues that vary according to market demands. Much has been made in relation to the control of FMS. Several studies in the literature cover issues of control of FMS. Despite the high number of papers that address these issues, technological advances and market demands cause introduction of new challenges and new studies are conducted. This work based itself on other studies about the control of FMS to evolve into some issues of modeling and control of FMS. The purpose of this paper is to indicate a modeling strategy, based on Petri nets, that is easy to understand, requiring low efforts to adapt to different production plans, and allowing that the control of the FMS model takes into account information of the current state of the system in decision making for conflict resolution. The work ends with the application of the modeling method proposed in a hypothetical FMS. It was built a control system to make the reading of variables from the model of the FMS and decide how conflicts should be resolved on the Petri net model. To support the decision making of the control system, it was built a Fuzzy system. Simulations were conducted for a variety of production plans in which the control system achieved real-time responses to conflict resolution, preventing the system of reach deadlock conditions.