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

We study control of constrained linear systems when faced with only partialstatistical information about the disturbance processes affecting the systemdynamics and the sensor measurements. Specifically, given a finite collectionof disturbance realizations, we consider the problem of designing a stabilizingcontrol policy with provable safety and performance guarantees in face of theinevitable mismatch between the true and the empirical distributions. Wecapture this discrepancy using Wasserstein ambiguity sets, and we formulate adistributionally robust (DR) optimal control problem, which provides guaranteeson the expected cost, safety, and stability of the system. To solve thisproblem, we first present new results for DR optimization of quadraticobjectives using convex programming, showing that strong duality holds undermild conditions. Then, by combining our results with the system levelparametrization (SLP) of linear feedback policies, we show that the designproblem can be reduced to a semidefinite optimization problem (SDP).

Distributionally Robust Infinite-horizon Control: from a pool of samples to the design of dependable controllers