Linear recurrent neural network for open- and closed-loop consistent identification of LPV models

Publikationstyp
Conference Paper
Date Issued
2010
Sprache
English
Author(s)
Abbas, Hossam El-Din Mahmoud Seddik  
Ali, Mukhtar  
Werner, Herbert  
Institut
Regelungstechnik E-14  
TORE-URI
http://hdl.handle.net/11420/14791
Journal
Proceedings of the IEEE Conference on Decision & Control  
Start Page
6851
End Page
6856
Article Number
5717855
Citation
Proceedings of the IEEE Conference on Decision and Control : 5717855 6851-6856 (2010)
Contribution to Conference
49th IEEE Conference on Decision and Control, CDC 2010  
Publisher DOI
10.1109/CDC.2010.5717855
Scopus ID
2-s2.0-79953135474
Publisher
IEEE
In this paper a Linear Recurrent Neural Network (LRNN) approach is used to consistently identify input-output Linear Parameter Varying (LPV) systems with additive output noise in input-output representation. Moreover, an indirect identification approach based on structured LRNN is proposed for consistent identification of input-output LPV models in closed-loop. The structured LRNN is trained to identify the closed-loop system from the reference to the output signal, where the controller parameters are presented as fixed weights and the parameters of the LPV model as unknown weights. The open-loop model can then be easily extracted from the identified closed-loop model. The proposed approach is illustrated with simulation examples, and a comparison with an existing approach is given. ©2010 IEEE.
DDC Class
600: Technik
620: Ingenieurwissenschaften