Abstract
A measurement-based statistical verification approach is developed for systems with partly unknown dynamics. These grey-box systems are subject to identification experiments which, new in this contribution, enable accepting or rejecting system properties expressed in a linear-time logic. We employ a Bayesian framework for the computation of a confidence level on the properties and for the design of optimal experiments. Applied to dynamical systems, this work enables data-driven verification of partly-known system dynamics with controllable non-determinism (inputs) and noisy output observations. A numerical case study concerning the safety of a dynamical system is used to elucidate this data-driven and model-based verification technique.
Original language | English |
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Title of host publication | Proceedings of the American Control Conference (ACC2015), 1-3 July 2015, Chicago, USA |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 1800-1805 |
Number of pages | 6 |
ISBN (Print) | 978-1-4799-8684-2 |
DOIs | |
Publication status | Published - 28 Jul 2015 |
Event | 2015 American Control Conference, ACC 2015 - Hilton Palmer House, Chicago, United States Duration: 1 Jul 2015 → 3 Jul 2015 http://acc2015.a2c2.org/ |
Conference
Conference | 2015 American Control Conference, ACC 2015 |
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Abbreviated title | ACC 2015 |
Country/Territory | United States |
City | Chicago |
Period | 1/07/15 → 3/07/15 |
Internet address |