Functional Dual Adaptive Control with Recursive Gaussian Process Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43926639" target="_blank" >RIV/49777513:23520/15:43926639 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1088/1742-6596/659/1/012006" target="_blank" >http://dx.doi.org/10.1088/1742-6596/659/1/012006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/659/1/012006" target="_blank" >10.1088/1742-6596/659/1/012006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Functional Dual Adaptive Control with Recursive Gaussian Process Model

Popis výsledku v původním jazyce

The paper deals with dual adaptive control problem, where the functional uncertainties in the system description are modelled by a non-parametric Gaussian process regression model. Current approaches to adaptive control based on Gaussian process models are severely limited in their practical applicability, because the model is re-adjusted using all the currently available data, which keeps growing with every time step. We propose the use of recursive Gaussian process regression algorithm for significant reduction in computational requirements, thus bringing the Gaussian process-based adaptive controllers closer to their practical applicability. In this work, we design a bi-criterial dual controller based on recursive Gaussian process model for discrete-time stochastic dynamic systems given in an affine-in-control form. Using Monte Carlo simulations, we show that the proposed controller achieves comparable performance with the full Gaussian process-based controller in terms of control quality while keeping the computational demands bounded.

Název v anglickém jazyce

Functional Dual Adaptive Control with Recursive Gaussian Process Model

Popis výsledku anglicky

The paper deals with dual adaptive control problem, where the functional uncertainties in the system description are modelled by a non-parametric Gaussian process regression model. Current approaches to adaptive control based on Gaussian process models are severely limited in their practical applicability, because the model is re-adjusted using all the currently available data, which keeps growing with every time step. We propose the use of recursive Gaussian process regression algorithm for significant reduction in computational requirements, thus bringing the Gaussian process-based adaptive controllers closer to their practical applicability. In this work, we design a bi-criterial dual controller based on recursive Gaussian process model for discrete-time stochastic dynamic systems given in an affine-in-control form. Using Monte Carlo simulations, we show that the proposed controller achieves comparable performance with the full Gaussian process-based controller in terms of control quality while keeping the computational demands bounded.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA15-12068S" target="_blank" >GA15-12068S: Adaptivní přístupy k odhadu stavu nelineárních stochastických systémů</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Journal of Physics: Conference Series, Volume 659

ISBN

—

ISSN

1742-6588

e-ISSN

1742-6596

Počet stran výsledku

11

Strana od-do

1-11

Název nakladatele

IOP Publishing

Místo vydání

Bristol

Místo konání akce

Plzeň, Česká Republika

Datum konání akce

19. 11. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000368103000006