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Structure independence of supervisor simplification in automated manufacturing systems using Petri nets

Authors: Chen Chen 0009; Hesuan Hu;

Structure independence of supervisor simplification in automated manufacturing systems using Petri nets

Abstract

For practical automated manufacturing systems, supervisory control techniques (SCTs) are of great significance and should be integrated in their plant models. SCTs are frequently accompanied by supervisor simplification issues. In the past, it is taken for granted that supervisor simplification is associated with systems' structure, because most simplification techniques are based on structure analysis. However, our study shows that it is actually independent from the systems' structure. This counter-intuitive statement is triggered by a simplification technique called inequality analysis, which is remarkably featured in three perspectives. First, it is a pure algebra way to simplify supervisor. Second, it can not only explain both strong and weak dependence by algebra analysis, but also the dependence that structure analysis cannot describe. Third, it is robust to inequality formation, structure change, and liveness control methods. Thanks to these features, supervisor simplification via inequality analysis becomes a systematic work irrelevant to the original systems and their liveness control. For better understanding, elementary-siphon-based technique in terms of structure analysis is utilized for comparison and discussion. Both the theoretical and experimental results validate the correctness of our statement.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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