Paper Info
Title
Compact representation of a nonlinear system using the NLPV approach
Abstract
Linear parameter varying (LPV) models are an extension of linear time varying systems as their parameters are expressed as a function of some scheduling variables: exogenous ones in the standard setup and internal ones in the case of so called quasi-LPV models. If the dependency on internal variables is chosen to be sufficiently general, quasi-LPV models boil down to a different representation of nonlinear systems. In contrast to classical nonlinear identification approaches, like artificial neural networks or NARMAX models, the NLPV approach offers the possibility to interpret the behavior of a complex system as the effect of a basically linear dynamic and a scheduling variable responsible for the nonlinearity. This paper proposes a new identification approach, whose main advantage lies in the fact that it presents a compact and precise nonlinear model with a small number of parameters. Our proposal yields to the time evolution of the scheduling variable which explains the nonlinear behavior of the system. The approach has been tested on an test bench with a diesel engine, experimental results are presented.
Year
DOI
Venue
2008
10.1109/CCA.2008.4629647
San Antonio, TX
Keywords
Field
DocType
linear systems,neurocontrollers,nonlinear control systems,time-varying systems,NARMAX models,NLPV approach,artificial neural networks,compact representation,diesel engines,linear dynamics,linear parameter varying models,linear time varying systems,nonlinear identification approaches,nonlinear system,scheduling variables
Nonlinear system,Test bench,Linear system,Control theory,Computer science,Scheduling (computing),Control engineering,Control system,Artificial neural network,Time complexity
Conference
ISSN
ISBN
Citations 
1085-1992
978-1-4244-2223-4
0
PageRank 
References 
Authors
0.34
3
4
Name
Order
Citations
PageRank
Giovanna Elizabeth Castillo Estrada100.34
Harald Kirchsteiger2335.62
Luigi del Re313131.55
del Re, L.45213.29