Data-Driven Permissible Safe Control with Barrier Certificates

Conference Paper (2025)
Author(s)

Rayan Mazouz (University of Colorado Boulder)

John Skovbekk (University of Colorado Boulder)

Frederik Baymler Mathiesen (TU Delft - Team Luca Laurenti)

Eric Frew (University of Colorado Boulder)

L. Laurenti (TU Delft - Team Luca Laurenti)

Morteza Lahijanian (University of Colorado Boulder)

Research Group
Team Luca Laurenti
DOI related publication
https://doi.org/10.1109/CDC56724.2024.10886850
More Info
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Publication Year
2025
Language
English
Research Group
Team Luca Laurenti
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.@en
Pages (from-to)
6844-6849
ISBN (electronic)
979-8-3503-1633-9
Reuse Rights

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Abstract

This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is learning the dynamics of the system via Gaussian Process (GP) regression and obtaining probabilistic errors for this estimate. Then, we develop an algorithm for constructing piecewise stochastic barrier functions to find a maximal permissible strategy set using the learned GP model, which is based on sequentially pruning the worst controls until a maximal set is identified. The permissible strategies are guaranteed to maintain probabilistic safety for the true system. This is especially important for learned systems, because a rich strategy space enables additional data collection and complex behaviors while remaining safe. Case studies on linear and nonlinear systems demonstrate that increasing the size of the dataset for learning grows the permissible strategy set.

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File under embargo until 26-08-2025