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 the simultaneous actuator selection and controller design problemfor linear quadratic regulation with Gaussian noise over a finite horizon oflength $T$ and unknown system model. We consider both episodic and non-episodicsettings of the problem and propose online algorithms that specify both thesets of actuators to be utilized under a cardinality constraint and thecontrols corresponding to the sets of selected actuators. In the episodicsetting, the interaction with the system breaks into $N$ episodes, each ofwhich restarts from a given initial condition and has length $T$. In thenon-episodic setting, the interaction goes on continuously. Our onlinealgorithms leverage a multiarmed bandit algorithm to select the sets ofactuators and a certainty equivalence approach to design the correspondingcontrols. We show that our online algorithms yield $\sqrt{N}$-regret for theepisodic setting and $T^{2/3}$-regret for the non-episodic setting. We extendour algorithm design and analysis to show scalability with respect to both thetotal number of candidate actuators and the cardinality constraint. Wenumerically validate our theoretical results.

Online Actuator Selection and Controller Design for Linear Quadratic Regulation with Unknown System Model