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 consider a class of stochastic optimal control problems for discrete-timestochastic linear systems which seek for control policies that will steer theprobability distribution of the terminal state of the system close to a desiredGaussian distribution. In our problem formulation, the closeness between theterminal state distribution and the desired (goal) distribution is measured interms of the squared Wasserstein distance which is associated with acorresponding terminal cost term. We recast the stochastic optimal controlproblem as a finite-dimensional nonlinear program and we show that itsperformance index can be expressed as the difference of two convex functions.This representation of the performance index allows us to find local minimizersof the original nonlinear program via the so-called convex-concave procedure[1]. Subsequently, we consider a similar problem but this time we use aterminal cost that corresponds to the KL divergence. Finally, we presentnon-trivial numerical simulations to demonstrate the proposed techniques andcompare them in terms of computation time.

Covariance Steering of Discrete-Time Stochastic Linear Systems Based on Distribution Distance Terminal Costs