Neural network transition enabling function petri net to supervisory control

摘要

This paper presents Petri Net (PN) models conceived to be employed with the Supervisory Control Theory (SCT). The SCT deals with the supervisory synthesis problem. In this paper, the supervisor synthesis is obtained by processing both the system and specification models through two algorithms, MRTA and ACGS respectively. These algorithms make possible to obtain the supervisor of a discrete event system (DES), modeled by a Petri Net with Neural Network Transition Enabling Function (PNTEF), based on a given specification. A supervisor of a manufacturing cell example is presented.