Integration of Artificial Neural Networks and linear systems for the output feedback control of nonlinear vibration systems

Javier G. Razuri, Antonio M. Cardenas, Rahim Rahmani, David Sundgren, Ikuo Mizuuchi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper analyzes the integration of neural networks and linear systems for the identification, state estimation and output feedback control of weakly nonlinear systems. Considering previous knowledge about the system given by approximated linear state-space models, linear observers and linear controllers, training algorithms for the neuro-identification, state neuro-estimation and output feedback neuro-control were derived considering the dynamics of the nonlinear system. It was found that the integrated linear-neuro model can identify the dynamics of the system much more accurately than a purely linear model or a purely neuro model. It was also found that the state estimation and vibration isolation performance of the system with integrated linear-neuro output feedback control is better than the system with linear control or neuro-control.

Original languageEnglish
Title of host publicationProceedings of the 33rd Chinese Control Conference, CCC 2014
EditorsShengyuan Xu, Qianchuan Zhao
PublisherIEEE Computer Society
Pages1850-1855
Number of pages6
ISBN (Electronic)9789881563842
DOIs
StatePublished - 11 Sep 2014
EventProceedings of the 33rd Chinese Control Conference, CCC 2014 - Nanjing, China
Duration: 28 Jul 201430 Jul 2014

Publication series

NameProceedings of the 33rd Chinese Control Conference, CCC 2014
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

ConferenceProceedings of the 33rd Chinese Control Conference, CCC 2014
Country/TerritoryChina
CityNanjing
Period28/07/1430/07/14

Keywords

  • Artificial Neural Networks
  • Control Theory
  • Linear Systems
  • Neuro-Control

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