English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

While many event-triggered control strategies are available in theliterature, most of them are designed ignoring the presence of measurementnoise. As measurement noise is omnipresent in practice and can have detrimentaleffects, for instance, by inducing Zeno behavior in the closed-loop system andwith that the lack of a positive lower bound on the inter-event times,rendering the event-triggered control design practically useless, it is ofgreat importance to address this gap in the literature. To do so, we present ageneral framework for set stabilization of (distributed) event-triggeredcontrol systems affected by additive measurement noise. It is shown that, undergeneral conditions, Zeno-free static as well as dynamic triggering rules can bedesigned such that the closed-loop system satisfies an input-to-state practicalset stability property. We ensure Zeno-freeness by proving the existence of auniform strictly positive lower-bound on the minimum inter-event time. Thegeneral framework is applied to point stabilization and consensus problems asparticular cases, where we show that, under similar assumptions as the originalwork, existing schemes can be redesigned to robustify them to measurementnoise. Consequently, using this framework, noise-robust triggering conditionscan be designed both from the ground up and by simple redesign of severalimportant existing schemes. Simulation results are provided that illustrate thestrengths of this novel approach.

Robustifying Event-Triggered Control to Measurement Noise