Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems

Andrea Peruffo*, Manuel Mazo

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Pages (from-to)115-120
JournalIEEE Control Systems Letters
Volume7
DOIs
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

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

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