Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems

Peruffo, A.; Mazo, M.

doi:10.1109/LCSYS.2022.3186187

Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems

Journal article (2022)

Authors

A. Peruffo Team Manuel Mazo Jr - Mechanical, Maritime and Materials Engineering

M. Mazo Team Manuel Mazo Jr - Mechanical, Maritime and Materials Engineering

Research Group

Team Manuel Mazo Jr (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI: https://doi.org/10.1109/LCSYS.2022.3186187

Stability analysis Automata Computational modeling Support vector machines Probabilistic logic Behavioral sciences Discrete event systems Picture archiving and communication systems Statistical learning

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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Manuel Mazo Jr

Abstract

We employ the scenario approach to compute probably approximately correct (PAC) bounds on the average inter-sample time (AIST) generated by an unknown PETC system, based on a finite number of samples. We extend the scenario optimisation to multiclass SVM algorithms in order to construct a PAC map between the concrete state-space and the inter-sample times. We then build a traffic model applying an l-complete relation and find, in the underlying graph, the cycles of minimum and maximum average weight: these provide lower and upper bounds on the AIST. Numerical benchmarks show the practical applicability of our method, which is compared against model-based state-of-the-art tools.

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