A hierarchical and modular structure for FMS control and monitoring

A hierarchical modular structure is presented for real-time control and monitoring of flexible manufacturing systems (FMSs), in which a clear distinction between processing of normal and unusual states is made. It is shown that this approach furthers autonomous diagnosis and recovery, because it makes it possible to integrate two techniques well suited in their own domains: Petri nets for the specification of normal sequences of control and AI techniques for dealing with diagnostic problems. The implementation techniques proposed are similar to rapid prototyping by execution of the specification (token player for Petri nets and inference engine for production rules)