ON-LINE ADAPTIVE NEURAL NETWORK IN VERY REMOTE CONTROL SYSTEM

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Abstract

Remote control involves several issues that degrade seriously the performance of the plant to be controlled. This paper presents a strategy improving the characteristics of the remote control system, using an on-line adaptive neural net, in order to learn the variations of the remote system parameters to minimize the errors. This strategy is successfully applied to a client-server remote control system for a two link robot arm. Tests show that an error position in a remote control brushless motor can be highly reduced since its first "reference command" using a prevision of that error to modify the original reference. The neural net, used only by the client, is previously trained using local test data and then it is trained using on-line feedback data front the remote plant.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2005

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cita questo

@conference{89218e857e30499aa71a26c6ca1919dd,
title = "ON-LINE ADAPTIVE NEURAL NETWORK IN VERY REMOTE CONTROL SYSTEM",
abstract = "Remote control involves several issues that degrade seriously the performance of the plant to be controlled. This paper presents a strategy improving the characteristics of the remote control system, using an on-line adaptive neural net, in order to learn the variations of the remote system parameters to minimize the errors. This strategy is successfully applied to a client-server remote control system for a two link robot arm. Tests show that an error position in a remote control brushless motor can be highly reduced since its first {"}reference command{"} using a prevision of that error to modify the original reference. The neural net, used only by the client, is previously trained using local test data and then it is trained using on-line feedback data front the remote plant.",
keywords = "Robots ; Remote control ; Mobile robots",
author = "Raimondi, {Francesco Maria} and Maurizio Melluso and Ciancimino, {Ludovico Salvatore}",
year = "2005",
language = "English",

}

TY - CONF

T1 - ON-LINE ADAPTIVE NEURAL NETWORK IN VERY REMOTE CONTROL SYSTEM

AU - Raimondi, Francesco Maria

AU - Melluso, Maurizio

AU - Ciancimino, Ludovico Salvatore

PY - 2005

Y1 - 2005

N2 - Remote control involves several issues that degrade seriously the performance of the plant to be controlled. This paper presents a strategy improving the characteristics of the remote control system, using an on-line adaptive neural net, in order to learn the variations of the remote system parameters to minimize the errors. This strategy is successfully applied to a client-server remote control system for a two link robot arm. Tests show that an error position in a remote control brushless motor can be highly reduced since its first "reference command" using a prevision of that error to modify the original reference. The neural net, used only by the client, is previously trained using local test data and then it is trained using on-line feedback data front the remote plant.

AB - Remote control involves several issues that degrade seriously the performance of the plant to be controlled. This paper presents a strategy improving the characteristics of the remote control system, using an on-line adaptive neural net, in order to learn the variations of the remote system parameters to minimize the errors. This strategy is successfully applied to a client-server remote control system for a two link robot arm. Tests show that an error position in a remote control brushless motor can be highly reduced since its first "reference command" using a prevision of that error to modify the original reference. The neural net, used only by the client, is previously trained using local test data and then it is trained using on-line feedback data front the remote plant.

KW - Robots ; Remote control ; Mobile robots

UR - http://hdl.handle.net/10447/12324

M3 - Paper

ER -