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.
|Title of host publication||Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021)|
|Publication status||Published - 2021|
|Event||60th IEEE Conference on Decision and Control (CDC 2021) - Austin, United States|
Duration: 14 Dec 2021 → 17 Dec 2021
|Conference||60th IEEE Conference on Decision and Control (CDC 2021)|
|Period||14/12/21 → 17/12/21|
Bibliographical noteGreen 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
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