Self-tuning Predictive Control of Nonlinear Servo-motor

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F10%3A63508985" target="_blank" >RIV/70883521:28140/10:63508985 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Self-tuning Predictive Control of Nonlinear Servo-motor

Popis výsledku v původním jazyce

The paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo ? motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle.Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model ? Amira DR300.

Název v anglickém jazyce

Self-tuning Predictive Control of Nonlinear Servo-motor

Popis výsledku anglicky

The paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo ? motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle.Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model ? Amira DR300.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

—

Projekt

<a href="/cs/project/1M0567" target="_blank" >1M0567: Centrum aplikované kybernetiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název periodika

Journal of Electrical Engineering

ISSN

1335-3632

e-ISSN

—

Svazek periodika

61

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

8

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—