TY - GEN
T1 - Abstracting the Sampling Behaviour of Stochastic Linear Periodic Event-Triggered Control Systems
AU - Delimpaltadakis, Giannis
AU - Laurenti, L.
AU - Mazo, M.
N1 - 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.
PY - 2021
Y1 - 2021
N2 - Recently, there have been efforts towards understanding the sampling behaviour of event-triggered control (ETC), for obtaining metrics on its sampling performance and predicting its sampling patterns. Finite-state abstractions, capturing the sampling behaviour of ETC systems, have proven promising in this respect. So far, such abstractions have been constructed for non-stochastic systems. Here, inspired by this framework, we abstract the sampling behaviour of stochastic narrow-sense linear periodic ETC (PETC) systems via Interval Markov Chains (IMCs). Particularly, we define functions over sequences of state-measurements and interevent times that can be expressed as discounted cumulative sums of rewards, and compute bounds on their expected values by constructing appropriate IMCs and equipping them with suitable rewards. Finally, we argue that our results are extendable to more general forms of functions, thus providing a generic framework to define and study various ETC sampling indicators.
AB - Recently, there have been efforts towards understanding the sampling behaviour of event-triggered control (ETC), for obtaining metrics on its sampling performance and predicting its sampling patterns. Finite-state abstractions, capturing the sampling behaviour of ETC systems, have proven promising in this respect. So far, such abstractions have been constructed for non-stochastic systems. Here, inspired by this framework, we abstract the sampling behaviour of stochastic narrow-sense linear periodic ETC (PETC) systems via Interval Markov Chains (IMCs). Particularly, we define functions over sequences of state-measurements and interevent times that can be expressed as discounted cumulative sums of rewards, and compute bounds on their expected values by constructing appropriate IMCs and equipping them with suitable rewards. Finally, we argue that our results are extendable to more general forms of functions, thus providing a generic framework to define and study various ETC sampling indicators.
UR - http://www.scopus.com/inward/record.url?scp=85122604688&partnerID=8YFLogxK
U2 - 10.1109/CDC45484.2021.9683751
DO - 10.1109/CDC45484.2021.9683751
M3 - Conference contribution
SN - 978-1-6654-3659-5
SP - 1287
EP - 1294
BT - Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021)
PB - IEEE
T2 - 60th IEEE Conference on Decision and Control (CDC 2021)
Y2 - 14 December 2021 through 17 December 2021
ER -