6533b821fe1ef96bd127c38c
RESEARCH PRODUCT
On-line adaptive neural network in very remote control system
Francesco Maria RaimondiL.s. CianciminoMaurizio Mellusosubject
EngineeringArtificial neural networkbusiness.industryReal-time computingControl engineeringRemote systemlaw.inventionBrushless motorsRemote controllawPosition (vector)Mobile robotsLine (text file)businessRobotsRobotic armRemote controlTest datadescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2006-04-07 | 2005 IEEE Conference on Emerging Technologies and Factory Automation |