Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems

Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems

Peruffo, A. (TU Delft Team Manuel Mazo Jr)
Mazo, M. (TU Delft Team Manuel Mazo Jr)

2022

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.

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

http://resolver.tudelft.nl/uuid:35492245-541e-4334-b402-31b06972df80

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

2022-12-24

2475-1456

IEEE Control Systems Letters, 7, 115-120

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.

Institutional Repository

journal article

© 2022 A. Peruffo, M. Mazo