A Robust Prevention Method for Automated Manufacturing Systems With Unreliable Resources Using Petri Nets

On the basis of the assumption that no resource failure occurs, a variety of deadlock control policies have been developed for automated manufacturing systems (AMSs). However, in practical manufacturing systems, the occurrence of resource failures is always inescapable. In case, if resources fail to...

Full description

Bibliographic Details
Main Authors: Nan Du, Hesuan Hu
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8561276/
Description
Summary:On the basis of the assumption that no resource failure occurs, a variety of deadlock control policies have been developed for automated manufacturing systems (AMSs). However, in practical manufacturing systems, the occurrence of resource failures is always inescapable. In case, if resources fail to work, the existing deadlock control strategies can no longer be applied to the changed system; therefore, redesigning a new strategy is necessary. Because of their powerful modeling capabilities, Petri nets are used to model the considered AMSs allowing multi-type and multi-quantity resource acquisition. This paper focuses on extending a deadlock prevention method to be applied to AMSs with unreliable resources. Strict minimal siphons are controlled by added control places (monitors) to ensure the system's liveness. To prevent blocking issues caused by resource failures, we develop a set of shared resource constraints represented by a set of inequalities based on the minimal resource requirements of processes and the capacity of shared resources. Robust monitors are designed for them to limit the distribution of tokens in unreliable neighborhood places. Our objective is to control resource allocation such that those parts not necessarily requiring any failed resource can continue progressing smoothly even if some unreliable resources break down. Examples are given to elucidate our proposed method clearly.
ISSN:2169-3536