TY - JOUR
T1 - Polynomial-complexity robust deadlock controllers for a class of automated manufacturing systems with unreliable resources using Petri nets
AU - Feng, Yanxiang
AU - Xing, Keyi
AU - Zhou, Meng Chu
AU - Chen, Hefeng
AU - Tian, Feng
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of P.R. China under Grant 61573278, and 61304052, in part by National Science Foundation for Post-doctoral Scientists of China under Grant 2018M643660, and in part by the Shandong Provincial Natural Science Foundation of China under Grant ZR2018MF024
Funding Information:
This work was supported in part by the National Natural Science Foundation of P.R. China under Grant 61573278, and 61304052, in part by National Science Foundation for Post-doctoral Scientists of China under Grant 2018M643660, and in part by the Shandong Provincial Natural Science Foundation of China under Grant ZR2018MF024
Publisher Copyright:
© 2020
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9
Y1 - 2020/9
N2 - In the context of automated manufacturing systems (AMSs) with unreliable resources, most existing robust deadlock controllers have high computational complexity or relatively low permissiveness. This work focuses on the deadlock control problem of AMSs with a kind of unreliable resources. Petri nets are used to model the dynamic behaviors of such failure-prone AMSs. First a robust deadlock prevention controller is developed for a large class of AMSs under consideration. Such a robust controller guarantees that the system can process all types of parts continuously through any one of their routes, even if one of unreliable resources fails. Also, this robust controller is proved to be optimal, i.e., maximally permissive, during one resource failure period. Then by using the one-step look-ahead method, we establish a polynomial-complexity robust deadlock avoidance policy (DAP) with the same permissiveness as the obtained robust deadlock prevention controller. That is, such a robust DAP not only has low computational complexity, but also is maximally permissive during one resource failure period.
AB - In the context of automated manufacturing systems (AMSs) with unreliable resources, most existing robust deadlock controllers have high computational complexity or relatively low permissiveness. This work focuses on the deadlock control problem of AMSs with a kind of unreliable resources. Petri nets are used to model the dynamic behaviors of such failure-prone AMSs. First a robust deadlock prevention controller is developed for a large class of AMSs under consideration. Such a robust controller guarantees that the system can process all types of parts continuously through any one of their routes, even if one of unreliable resources fails. Also, this robust controller is proved to be optimal, i.e., maximally permissive, during one resource failure period. Then by using the one-step look-ahead method, we establish a polynomial-complexity robust deadlock avoidance policy (DAP) with the same permissiveness as the obtained robust deadlock prevention controller. That is, such a robust DAP not only has low computational complexity, but also is maximally permissive during one resource failure period.
KW - Automated manufacturing systems (AMSs)
KW - Discrete event systems
KW - Petri nets
KW - Robust deadlock control
KW - Unreliable resources
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U2 - 10.1016/j.ins.2020.05.007
DO - 10.1016/j.ins.2020.05.007
M3 - Article
AN - SCOPUS:85085754336
SN - 0020-0255
VL - 533
SP - 181
EP - 199
JO - Information sciences
JF - Information sciences
ER -