9400146 Choobineh This research will develop a standard methodology for creating effective Flexible Manufacturing System (FMS) controllers, using a combination of Fuzzy Logic (FL) and Neural Network (NN) technologies. This combination is known as a Neuro-Fuzzy Logic Controller (NFLC), and holds promise in the control of automated manufacturing systems. To facilitate the development of an NFLC, Generalized Stochastic Petri Nets (GSPNs) will be used. Specifically the GSPN of an FMS will be partitioned into two subnets - one for the physical plant, and one for the controller. The productivity of an FMS is highly dependent upon the effectiveness and efficiency of the controller algorithms employed. Current FMS controllers fall substantially short on these measures.
