Synthesis of feedback control logic for discrete manufacturing systems
Automatica (Journal of IFAC)
Feedback control of Petri nets based on place invariants
Automatica (Journal of IFAC)
A Survey of Petri Net Methods for Controlled Discrete EventSystems
Discrete Event Dynamic Systems
Reduction of constraints for controller synthesis based on safe Petri Nets
Automatica (Journal of IFAC)
Computer Standards & Interfaces
Combined siphon and marking generation for deadlock prevention in Petri nets
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Selective Siphon Control for Deadlock Prevention in Petri Nets
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 22.14 |
Petri net (PN) supervisory control is often performed through a sequential procedure that introduces additional constraint layers over an initial unconstrained PN model, using generalized mutual exclusion constraints (GMECs) implemented as monitor places. This is typical, e.g., in the context of flexible manufacturing systems, where the initial model represents the production sequences and the constraints are used to express static specifications, such as job limitations or the usage of resources, and behavioral ones, as liveness, controllability, etc. This sequential procedure may yield a redundant model, that is not easily reduced a posteriori. Also, it is difficult to ensure maximal permissivity with respect to multiple behavioral specifications. This paper, building on recent results regarding optimal supervisor design with branch & bound methods, proposes an integrated modeling approach that can be used to derive a minimal supervisor guaranteeing the attainment of an arbitrary set of static and behavioral specifications in a maximally permissive way. Among behavioral specifications, deadlock-freeness, liveness, reversibility and behavioral controllability are considered in the paper. The supervisor comes in the form of a simple set of GMECs or of a disjunction of sets of GMECs. Some examples emphasize the potential model size reductions that can be achieved.