Model reference multiple-degree-of-freedom adaptive control with HONUs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F16%3A00305514" target="_blank" >RIV/68407700:21220/16:00305514 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7727843/" target="_blank" >http://ieeexplore.ieee.org/document/7727843/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN.2016.7727843" target="_blank" >10.1109/IJCNN.2016.7727843</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model reference multiple-degree-of-freedom adaptive control with HONUs

Popis výsledku v původním jazyce

This paper presents a modification of reference-model adaptive control with a layered network of higher-order neural units (HONUs) as adaptive state-feedback controllers. The degree of freedom of such neural controller is deemed here as the number of applied HONUs of a customizable polynomial order and as of their individually customizable input vectors. Furthermore, the control scheme is enhanced because potentially occurring disturbances of controlled variable can result in that the sample-by-sample adapted controllers may tend to adaptively force the plant output to merely follow the reference model output because input data are affected by the error of disturbance, while the overall control loop dynamics would be more accurately adapted if no perturbation occurs or if the reference model is actuated with actually measured variables. Furthermore, the controller weight updates usually involve some step-delayed computations that might be in fact recalculated with the latest updated weights, so the controller learning can be improved.

Název v anglickém jazyce

Model reference multiple-degree-of-freedom adaptive control with HONUs

Popis výsledku anglicky

This paper presents a modification of reference-model adaptive control with a layered network of higher-order neural units (HONUs) as adaptive state-feedback controllers. The degree of freedom of such neural controller is deemed here as the number of applied HONUs of a customizable polynomial order and as of their individually customizable input vectors. Furthermore, the control scheme is enhanced because potentially occurring disturbances of controlled variable can result in that the sample-by-sample adapted controllers may tend to adaptively force the plant output to merely follow the reference model output because input data are affected by the error of disturbance, while the overall control loop dynamics would be more accurately adapted if no perturbation occurs or if the reference model is actuated with actually measured variables. Furthermore, the controller weight updates usually involve some step-delayed computations that might be in fact recalculated with the latest updated weights, so the controller learning can be improved.

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of International Joint Conference on Neural Networks 2016

ISBN

9781509006199

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

4895-4900

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Vancouver

Datum konání akce

24. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—