Eventos Anais de eventos
COBEM 2017
24th ABCM International Congress of Mechanical Engineering
Automation learning environment based on fuzzy systems
Submission Author:
Sildenir Alves Ribeiro , RJ , Brazil
Co-Authors:
Jair Medeiros, Ronilson pinho , Nival Rodrigues, Luiz Bionde, Sildenir Alves Ribeiro
Presenter: Jair Medeiros
doi://10.26678/ABCM.COBEM2017.COB17-1221
Abstract
The way of handling information and transmitting knowledge has led to substantial changes in the training of the engineering professional. Technological advancement and the adoption of teaching practices aligned with technological resources have played an important role in this new scenario. Thus, this proposal aims to present a practical learning environment using a nebulous logic that simulates the industrial environment with low cost and easy adaptability to existing environments. With this, the model can be implemented in any institution of higher education. Another important feature is that the model can be applied at the levels of technical, undergraduate and graduate education in the area of control and automation. The proposed model was conceived after a research with professors and students of undergraduate courses in control engineering and automation and electrical engineering from three universities of Rio de Janeiro (UERJ, PUC and UFRJ). Field research was also carried out to identify the mechanisms and instruments needed to build the models of the learning environment suggested by the interviewees. As a computational interface, LABVIEW was used for the easy applicability of controls and also for supporting a practical-industrial environment. The plant was constituted of typical elements for the practice of control and automation, as sensors, actuators, and electric motor, besides a computational platform. The tests were performed in three learning environments: (1) thermal system; (2) engine speed control; And (3) inverted pendulum. In all three cases, a fuzzy controller was used to characterize the academic-industrial activity.
Keywords
Learning environment, Automation and Control, Fuzzy Systems, Practical Models
