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

Direct data-driven control has attracted substantial interest since itenables optimization-based control without the need for a parametric model.This paper presents a new Instrumental Variable~(IV) approach to Data-enabledPredictive Control (DeePC) that results in favorable noise mitigationproperties, and demonstrates the direct equivalence between DeePC and SubspacePredictive Control (SPC). The methodology relies on the derivation of thecharacteristic equation in DeePC along the lines of subspace identificationalgorithms. A particular choice of IVs is presented that is uncorrelated withfuture noise, but at the same time highly correlated with the data matrix. Asimulation study demonstrates the improved performance of the proposedalgorithm in the presence of process and measurement noise.

Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control