Implementação de sistemas de controle difuso em uma planta didática

• Autor(a) principal: Grimaldos, Miguel Angel Ahumedo
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFS
• Texto Completo: https://ri.ufs.br/handle/riufs/5030
• topic: Engenharia elétrica; Sistemas difusos; Lógica difusa; Controle difuso; Controle difuso-PID; OPC-HSE planta didática; Controle de temperatura; Fuzzy control; Fuzzy-PID control; OPC-HSE; Didactic plant; Temperature control; ENGENHARIAS::ENGENHARIA ELETRICA

The present work aims to expand the possibilities of PD 3 control strategies implementation and to evaluate, in a practical way, different strategies of fuzzy logic application in the control of processes of a didactic plant (PD 3). This was accomplished by adding fuzzy control strategies to the conventional industrial control strategies with which the PD 3 system is factory configured. The conventional strategies use PID controllers and its variations (P, PI and PD) which parameters are preset and "implanted" in the instruments. Three fuzzy control strategies were performed with temperature as the main process variable to be controlled and flow as the secondary variable to be controlled. The first strategy was the fuzzy-PID temperature control; it was used the local PID controller of PD 3 and a strategy was developed in fuzzy logic in an external workstation, for tuning of the PID parameters in real time. The PD 3 controller acts directly on the power of the heating resistances, which is the manipulated variable. This way, the temperature was controlled in the process. The second strategy, called temperature fuzzy-PID + flow, continued using the same fuzzy-PID temperature controller but was added a fuzzy control to regulate the process flow variable. This fuzzy control was developed at the work station and directly manipulates the actuator of the intelligent water inlet control valve to the heating tank. Finally, a completely fuzzy strategy was developed to control temperature and flow variables of the process. Of the three strategies implemented, completely fuzzy controller presented the best response when observed all metrics and performance indicators.