Self-Triggered Control for Near-Maximal Average Inter-Sample Time

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Self-triggered control (STC) is a sample-and-hold control method aimed at reducing communications in networked-control systems; however, existing STC mechanisms often maximize how late the next sample is, thus not optimizing sampling performance in the long-term. In this work, we devise a method to construct self-triggered policies that provide near-maximal average inter-sample time (AIST) while respecting given control performance constraints. To achieve this, we rely on finite-state abstractions of a reference event-triggered control, while also allowing earlier samples. These early triggers constitute controllable actions of the abstraction, for which an AIST-maximizing strategy can be obtained by solving a mean-payoff game. We provide optimality bounds, and how to further improve them through abstraction refinement techniques.
Original languageEnglish
Title of host publicationProceedings of the 60th IEEE Conference on Decision and Control (CDC 2021)
PublisherIEEE
Pages1308-1313
ISBN (Print)978-1-6654-3659-5
DOIs
Publication statusPublished - 2021
Event60th IEEE Conference on Decision and Control (CDC 2021) - Austin, United States
Duration: 14 Dec 202117 Dec 2021

Conference

Conference60th IEEE Conference on Decision and Control (CDC 2021)
Country/TerritoryUnited States
CityAustin
Period14/12/2117/12/21

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.

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