An adaptive PID neural network for complex nonlinear system control

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092746" target="_blank" >RIV/61989100:27240/14:86092746 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092746

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.neucom.2013.03.065" target="_blank" >http://dx.doi.org/10.1016/j.neucom.2013.03.065</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2013.03.065" target="_blank" >10.1016/j.neucom.2013.03.065</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An adaptive PID neural network for complex nonlinear system control

Popis výsledku v původním jazyce

Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced toinitialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjustthe network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods.

Název v anglickém jazyce

An adaptive PID neural network for complex nonlinear system control

Popis výsledku anglicky

Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced toinitialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjustthe network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

135

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

7

Strana od-do

79-85

Kód UT WoS článku

000335871200010

EID výsledku v databázi Scopus

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