Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems
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Abstract (arXiv)
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 approach to multiclass SVM algorithms in order to construct a PAC map between the concrete, unknown 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 10 Mar 2022.
Builds on
9 of 25 references.
References · 9
- 2021Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noisecited by 13
- 2019Scenario-Based Verification of Uncertain MDPscited by 6
- 2021Symbolic Abstractions From Data: A PAC Learning Approachcited by 6
- 2015A Formal Traffic Characterization of LTI Event-triggered Control Systemscited by 1
- 2020Scalable Traffic Models for Scheduling of Linear Periodic Event-Triggered Controllerscited by 1
- 2020Towards Traffic Bisimulation of Linear Periodic Event-Triggered Controllerscited by 1
- 2021Computing the sampling performance of event-triggered controlcited by 1
- 2022Chaos and order in event-triggered controlcited by 1
- 2022ETCetera: beyond Event-Triggered Controlcited by 1
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Citing papers per year: 2022: 1.
1 paper.