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Model-bounded monitoring of hybrid systems

Abstract

Monitoring of hybrid systems attracts both scientific and practical attention. However, monitoring algorithms suffer from the methodological difficulty of only observing sampled discrete-time signals, while real behaviors are continuous-time signals. To mitigate this problem of sampling uncertainties, we introduce a model-bounded monitoring scheme, where we use prior knowledge about the target system to prune interpolation candidates. Technically, we express such prior knowledge by linear hybrid automata (LHAs) - the LHAs are called bounding models. We introduce a novel notion of monitored language of LHAs, and we reduce the monitoring problem to the membership problem of the monitored language. We present two partial algorithms - one is via reduction to reachability in LHAs and the other is a direct one using polyhedra - and show that these methods, and thus the proposed model-bounded monitoring scheme, are efficient and practically relevant.

Dates and versions

hal-03142412 , version 1 (16-02-2021)

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Masaki Waga, Étienne André, Ichiro Hasuo. Model-bounded monitoring of hybrid systems. ICCPS 2021 - 12th ACM/IEEE International Conference on Cyber-Physical Systems, Martina Maggio; James Weimer, May 2021, Nashville, United States. ⟨hal-03142412⟩
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