Hierarchical Supervisory Control under Partial Observation: Normality
Hierarchical Supervisory Control under Partial Observation: Normality
Hierarchical Supervisory Control under Partial Observation: Normality
Abstract
Abstract
Conditions preserving observability of specifications between the plant and its abstraction are essential for hierarchical supervisory control of discrete-event systems under partial observation. Observation consistency and local observation consistency were identified as such conditions. To preserve normality, only observation consistency is required. Although observation consistency preserves normality between the levels for normal specifications, for specifications that are not normal, observation consistency is insufficient to guarantee that the supremal normal sublanguage computed on the low level and on the high level coincide. We define modified observation consistency, under which the supremal normal sublanguages of different levels coincide. We show that the verification of (modified) observation consistency is PSPACE-hard for finite automata and undecidable for slightly more expressive models than finite automata. Decidability of (modified) observation consistency is an open problem. Hence we further discuss two stronger conditions that are easy to verify. Finally, we illustrate the conditions on an example of a railroad controller and on a case study of a part of an MRI scanner.
Projects
Projects
Verification and Control of Networked Discrete Event Systems
Verification and Control of Networked Discrete Event Systems
Tomáš Masopust, Jan Komenda
Jiří Balun, Stéphane Lafortune, Spyros Reveliotis, Feng Lin
Compositional Methods for the Control of Concurrent Timed Discrete-Event Systems
Compositional Methods for the Control of Concurrent Timed Discrete-Event Systems
Jan Komenda, Jörg Raisch
Tomáš Masopust, Thomas Moor
Links
Links
Authors
Authors
Jan KomendaTomáš Masopust
IEEE Transactions on Automatic Control 68(12), 7286-7298, 2023
Discrete-Event Systems and Theoretical Computer Science Research Group
DOI
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