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 propose a methodology for performing risk-averse quadratic regulation ofpartially observed Linear Time-Invariant (LTI) systems disturbed by process andoutput noise. To compensate against the induced variability due to both typesof noises, state regulation is subject to two risk constraints. The latterrenders the resulting controller cautious of stochastic disturbances, byrestricting the statistical variability, namely, a simplified version of thecumulative expected predictive variance of both the state and the output. Ourproposed formulation results in an optimal risk-averse policy that preservesfavorable characteristics of the classical Linear Quadratic (LQ) control. Inparticular, the optimal policy has an affine structure with respect to theminimum mean square error (mmse) estimates. The linear component of the policyregulates the state more strictly in riskier directions, where the process andoutput noise covariance, cross-covariance, and the corresponding penalties aresimultaneously large. This is achieved by "inflating" the state penalty in asystematic way. The additional affine terms force the state against pure andcross third-order statistics of the process and output disturbances. Anotherfavorable characteristic of our optimal policy is that it can be pre-computedoff-line, thus, avoiding limitations of prior work. Stability analysis showsthat the derived controller is always internally stable regardless of parametertuning. The functionality of the proposed risk-averse policy is illustratedthrough a working example via extensive numerical simulations.

State-Output Risk-Constrained Quadratic Control of Partially Observed Linear Systems