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

This paper investigates the design of self-triggered control for networkedcontrol systems (NCS), where the dynamics of the plant is unknown apriori. Todeal with the nature of the self-triggered control, in which state measurementsare transmitted to the controller a-periodically, we propose to lift thecontinuous-time dynamics to a novel dynamical model by taking an inter-eventtime as an additional input, and then, the lifted model is learned by theGaussian processes (GP) regression. Moreover, we propose a learning-basedapproach, in which a self-triggered controller is learned by minimizing a costfunction, such that it can take inter-sample behavior into account. Byemploying the lifting approach, we can utilize a gradient-based policy updateas an efficient method to optimize both control and communication policies.Finally, we summarize the overall algorithm and provide a numerical simulationto illustrate the effectiveness of the proposed approach.

A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes