MODEL FOR DECISION-MAKING ON ACCESS CONTROL TO REMOTE LABORATORY PRACTICES BASED ON FUZZY COGNITIVE MAPS

Abstract

The operations of Industrial systems require to maintaining the stability of certain operation parameters. With this aim were developed the control systems. They allow the operation of processes automatically, replacing human procedures of measurement and intervention. Development of automation on facing the demands of the different industrial processes that required modern methods of control. Training the professionals to meet the demands of automation in Cuba is carried out from the Automation Engineering. In this context, the Control Systems is a critical discipline in the academic formation of graduated ones. For the correct development of the Control Engineering matter included into the Control Systems discipline, laboratory practices as a kind of lesson play an important role. The user (student) can do the practices in two platforms: by using physical laboratories with equipment related to the received subject, and/ or using a distance or remote way. The use of a Remote Laboratory System for laboratory practices introduces problems in terms of supervising the work carried out by the students, because is not possible to have a full-time supervisor for this function. This research presents a model for decision-making on access control to laboratory practices. The proposal is described through a workflow with four components. Artificial intelligence techniques were used to model causal knowledge using Fuzzy Cognitive Maps. For the validation of the model, several methods and techniques were used. The proposed experimental design demonstrated the correlation of the research variables.