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Integration of Artificial Neural Networks and Linear Systems for the Output Feedback Control of Nonlinear Vibration Systems
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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2014 (English)In: CCC 2014, the 33rd Chinese Control Conference, IEEE Press, 2014, 1850-1855 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE Press, 2014. 1850-1855 p.
Keyword [en]
Artificial Neural Networks, Linear Systems, Control Theory, Neuro-Control
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-111007DOI: 10.1109/ChiCC.2014.6896911OAI: oai:DiVA.org:su-111007DiVA: diva2:773781
Conference
CCC 2014, the 33rd Chinese Control Conference,28-30 July 2014, Nanjing
Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2015-08-12Bibliographically approved

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Guerrero Rázuri, Javier FranciscoRahmani, RahimSundgren, David
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